diff --git a/.github/actions/install-macos-dependencies/action.yml b/.github/actions/install-macos-dependencies/action.yml
index b19cac1052..95ca82a26e 100644
--- a/.github/actions/install-macos-dependencies/action.yml
+++ b/.github/actions/install-macos-dependencies/action.yml
@@ -27,5 +27,8 @@ runs:
 
     # install amici dependencies
     - name: homebrew
-      run: brew install hdf5 swig gcc libomp boost
+      # install hdf5 without dependencies, because pkgconf installation fails,
+      # because it's already installed on the runners. install the other
+      # hdf5 dependencies (libaec) manually
+      run: brew install libaec && brew install --ignore-dependencies hdf5 && brew install swig libomp boost
       shell: bash
diff --git a/.github/workflows/test_benchmark_collection_models.yml b/.github/workflows/test_benchmark_collection_models.yml
index 3e46af8992..e68ec1518b 100644
--- a/.github/workflows/test_benchmark_collection_models.yml
+++ b/.github/workflows/test_benchmark_collection_models.yml
@@ -50,7 +50,7 @@ jobs:
       run: |
         pip3 install --user petab[vis] && \
         AMICI_PARALLEL_COMPILE="" pip3 install -v --user \
-            $(ls -t python/sdist/dist/amici-*.tar.gz | head -1)[petab,test,vis]
+            $(ls -t python/sdist/dist/amici-*.tar.gz | head -1)[petab,test,vis,jax]
 
     - name: Install test dependencies
       run: |
@@ -60,14 +60,27 @@ jobs:
 
     - name: Download benchmark collection
       run: |
-        git clone --depth 1 https://github.com/benchmarking-initiative/Benchmark-Models-PEtab.git \
-          && python3 -m pip install -e Benchmark-Models-PEtab/src/python
+        pip install git+https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab.git@master#subdirectory=src/python
 
     - name: Run tests
       env:
         AMICI_PARALLEL_COMPILE: ""
       run: |
-          cd tests/benchmark-models && pytest --durations=10
+          cd tests/benchmark-models && pytest \
+              --durations=10
+              --cov=amici \
+              --cov-report=xml:"coverage_py.xml" \
+              --cov-append \
+
+    - name: Codecov Python
+      if: github.event_name == 'pull_request' || github.repository_owner == 'AMICI-dev'
+      uses: codecov/codecov-action@v5
+      with:
+        token: ${{ secrets.CODECOV_TOKEN }}
+        file: coverage_py.xml
+        flags: python
+        fail_ci_if_error: true
+        verbose: true
 
     # collect & upload results
     - name: Aggregate results
diff --git a/.github/workflows/test_install.yml b/.github/workflows/test_install.yml
index 9e1717d962..cc4cb595e5 100644
--- a/.github/workflows/test_install.yml
+++ b/.github/workflows/test_install.yml
@@ -96,14 +96,8 @@ jobs:
 
     - run: echo "AMICI_DIR=$(pwd)" >> $GITHUB_ENV
 
-    # install amici dependencies
-    - name: homebrew
-      run: |
-        brew install hdf5 swig gcc cppcheck libomp boost \
-          && brew ls -v boost \
-          && brew ls -v libomp \
-          && echo LDFLAGS="-L/usr/local/lib/ -L/usr/local/Cellar/boost/1.81.0_1/lib/" >> $GITHUB_ENV \
-          && echo CPPFLAGS="-I /usr/local/Cellar/boost/1.81.0_1/include/" >> $GITHUB_ENV
+    - name: Install dependencies
+      uses: ./.github/actions/install-macos-dependencies
 
     - name: Create AMICI sdist
       run: scripts/buildSdist.sh
diff --git a/.github/workflows/test_petab_test_suite.yml b/.github/workflows/test_petab_test_suite.yml
index 7e86a70d66..265d8c429a 100644
--- a/.github/workflows/test_petab_test_suite.yml
+++ b/.github/workflows/test_petab_test_suite.yml
@@ -101,7 +101,7 @@ jobs:
 
       - name: Codecov
         if: github.event_name == 'pull_request' || github.repository_owner == 'AMICI-dev'
-        uses: codecov/codecov-action@v4
+        uses: codecov/codecov-action@v5
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
           file: coverage.xml
diff --git a/.github/workflows/test_python_cplusplus.yml b/.github/workflows/test_python_cplusplus.yml
index fcb1067b85..85e2ee964a 100644
--- a/.github/workflows/test_python_cplusplus.yml
+++ b/.github/workflows/test_python_cplusplus.yml
@@ -79,7 +79,7 @@ jobs:
 
     - name: Codecov Python
       if: github.event_name == 'pull_request' || github.repository_owner == 'AMICI-dev'
-      uses: codecov/codecov-action@v4
+      uses: codecov/codecov-action@v5
       with:
         token: ${{ secrets.CODECOV_TOKEN }}
         file: build/coverage_py.xml
@@ -99,7 +99,7 @@ jobs:
 
     - name: Codecov CPP
       if: github.event_name == 'pull_request' || github.repository_owner == 'AMICI-dev'
-      uses: codecov/codecov-action@v4
+      uses: codecov/codecov-action@v5
       with:
         token: ${{ secrets.CODECOV_TOKEN }}
         file: coverage.info
@@ -161,7 +161,7 @@ jobs:
 
     - name: Codecov Python
       if: github.event_name == 'pull_request' || github.repository_owner == 'AMICI-dev'
-      uses: codecov/codecov-action@v4
+      uses: codecov/codecov-action@v5
       with:
         token: ${{ secrets.CODECOV_TOKEN }}
         file: build/coverage_py.xml
@@ -181,7 +181,7 @@ jobs:
 
     - name: Codecov CPP
       if: github.event_name == 'pull_request' || github.repository_owner == 'AMICI-dev'
-      uses: codecov/codecov-action@v4
+      uses: codecov/codecov-action@v5
       with:
         token: ${{ secrets.CODECOV_TOKEN }}
         file: coverage.info
@@ -231,11 +231,6 @@ jobs:
     - name: Install python package
       run: scripts/installAmiciSource.sh
 
-    - name: Install notebook dependencies
-      run: |
-        source venv/bin/activate \
-        && pip install jax[cpu]
-
     - name: example notebooks
       run: scripts/runNotebook.sh python/examples/example_*/
 
diff --git a/.github/workflows/test_sbml_semantic_test_suite.yml b/.github/workflows/test_sbml_semantic_test_suite.yml
index 69c78d44b4..f09e59c93f 100644
--- a/.github/workflows/test_sbml_semantic_test_suite.yml
+++ b/.github/workflows/test_sbml_semantic_test_suite.yml
@@ -55,7 +55,7 @@ jobs:
 
     - name: Codecov SBMLSuite
       if: github.event_name == 'pull_request' || github.repository_owner == 'AMICI-dev'
-      uses: codecov/codecov-action@v4
+      uses: codecov/codecov-action@v5
       with:
         token: ${{ secrets.CODECOV_TOKEN }}
         file: coverage_SBMLSuite.xml
diff --git a/.github/workflows/test_valgrind.yml b/.github/workflows/test_valgrind.yml
index b3f893647f..ef5ef9ba95 100644
--- a/.github/workflows/test_valgrind.yml
+++ b/.github/workflows/test_valgrind.yml
@@ -89,5 +89,9 @@ jobs:
     - name: Install python package
       run: scripts/installAmiciSource.sh
 
+    - name: Remove jax
+      # avoid valgrind errors due to jax et al.
+      run: venv/bin/pip uninstall -y jax
+
     - name: Python tests / Valgrind
       run: scripts/run-valgrind-py.sh
diff --git a/.gitignore b/.gitignore
index 0faae713ab..e68c2e4f72 100644
--- a/.gitignore
+++ b/.gitignore
@@ -137,6 +137,8 @@ tests/test/*
 */tests/explicit_amici/*
 */tests/fixed_initial_amici/*
 */tests/localfunc_amici/*
+*/tests/conversion/*
+*/tests/dimerization/*
 tests/cpp/writeResults.h5
 tests/cpp/writeResults.h5.bak
 tests/sbml-test-suite/*
diff --git a/.readthedocs.yml b/.readthedocs.yml
index 23cc6addeb..9b63ce6dce 100644
--- a/.readthedocs.yml
+++ b/.readthedocs.yml
@@ -21,6 +21,7 @@ build:
   os: "ubuntu-22.04"
   apt_packages:
     - libatlas-base-dev
+    - libhdf5-serial-dev
     - swig
   tools:
     python: "3.11"
diff --git a/CHANGELOG.md b/CHANGELOG.md
index d4c667b7c7..3a46670e10 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,6 +4,60 @@ See also our [versioning policy](https://amici.readthedocs.io/en/latest/versioni
 
 ## v0.X Series
 
+### v0.29.0 (2024-11-28)
+
+**Fixes**
+
+* Fixed race conditions in froot, which could have resulted in incorrect
+  simulation results for models with events/heavisides/piecewise, for
+  multi-threaded simulations.
+
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2587
+
+* Fixed race conditions for the max-time check, which could have resulted in
+  incorrect termination of simulations in case of multi-threaded simulations
+  in combination with a time limit.
+
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2587
+
+* Added missing fields in ExpData HDF5 I/O
+
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2593
+
+* Added missing fields in ReturnData HDF5 output
+
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2602
+
+
+* **Features**
+
+* Generate models in a JAX-compatible format
+  ([example](https://amici.readthedocs.io/en/develop/ExampleJaxPEtab.html))
+
+  by @FFroehlich in https://github.com/AMICI-dev/AMICI/pull/1861
+
+* Faster `fill_in_parameters_for_condition`
+
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2586
+
+* Added Python function `writeSimulationExpData` for writing ExpData to HDF5
+
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2588
+
+* Improved import of amici-generated models via `amici.import_model_module()`.
+
+  So far, it was not possible to import different model modules with the same
+  name. This is now possible if they are in different directories.
+  Overwriting an already imported module is still not possible (and never
+  was); any attempts to do so will raise a `RuntimeError`.
+  While model packages can, in principle, be imported using regular
+  `import`s, it is strongly recommended to use `amici.import_model_module()`.
+
+  by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2604, https://github.com/AMICI-dev/AMICI/pull/2603, https://github.com/AMICI-dev/AMICI/pull/2596
+
+**Full Changelog**: https://github.com/AMICI-dev/AMICI/compare/v0.28.0...v0.29.0
+
+
 ### v0.28.0 (2024-11-11)
 
 **Breaking changes**
diff --git a/documentation/ExampleJaxPEtab.ipynb b/documentation/ExampleJaxPEtab.ipynb
new file mode 120000
index 0000000000..821b14f21f
--- /dev/null
+++ b/documentation/ExampleJaxPEtab.ipynb
@@ -0,0 +1 @@
+../python/examples/example_jax_petab/ExampleJaxPEtab.ipynb
\ No newline at end of file
diff --git a/documentation/conf.py b/documentation/conf.py
index c86a145f9d..4445c62069 100644
--- a/documentation/conf.py
+++ b/documentation/conf.py
@@ -206,6 +206,7 @@ def install_doxygen():
     "numpy": ("https://numpy.org/devdocs/", None),
     "sympy": ("https://docs.sympy.org/latest/", None),
     "python": ("https://docs.python.org/3", None),
+    "jax": ["https://jax.readthedocs.io/en/latest/", None],
 }
 
 # Add notebooks prolog with binder links
diff --git a/documentation/cpp_installation.rst b/documentation/cpp_installation.rst
index a7165ce83a..7e464122b7 100644
--- a/documentation/cpp_installation.rst
+++ b/documentation/cpp_installation.rst
@@ -18,6 +18,7 @@ Prerequisites:
 * a C++17 compatible compiler
 * a C compiler
 * Optional:
+
   * HDF5 libraries
   * boost for serialization
 
diff --git a/documentation/python_examples.rst b/documentation/python_examples.rst
index 286ebf3ffd..fd1163690e 100644
--- a/documentation/python_examples.rst
+++ b/documentation/python_examples.rst
@@ -17,5 +17,6 @@ Various example notebooks.
    example_errors.ipynb
    example_large_models/example_performance_optimization.ipynb
    ExampleJax.ipynb
+   ExampleJaxPEtab.ipynb
    ExampleSplines.ipynb
    ExampleSplinesSwameye2003.ipynb
diff --git a/documentation/python_installation.rst b/documentation/python_installation.rst
index d82a4708e8..eb4d87d59c 100644
--- a/documentation/python_installation.rst
+++ b/documentation/python_installation.rst
@@ -39,7 +39,7 @@ Install the AMICI dependencies via ``apt``
 
 .. code-block:: bash
 
-   sudo apt install libatlas-base-dev swig
+   sudo apt install libatlas-base-dev swig python3-dev
 
    # optionally for HDF5 support:
    sudo apt install libhdf5-serial-dev
diff --git a/documentation/python_modules.rst b/documentation/python_modules.rst
index 2607447f0d..096dd0735f 100644
--- a/documentation/python_modules.rst
+++ b/documentation/python_modules.rst
@@ -25,6 +25,7 @@ AMICI Python API
    amici.petab_objective
    amici.petab_simulate
    amici.import_utils
+   amici.jax
    amici.de_export
    amici.de_model
    amici.de_model_components
diff --git a/documentation/rtd_requirements.txt b/documentation/rtd_requirements.txt
index 05e04bc957..54a35f9f94 100644
--- a/documentation/rtd_requirements.txt
+++ b/documentation/rtd_requirements.txt
@@ -3,6 +3,8 @@ sphinx<8
 mock>=5.0.2
 setuptools>=67.7.2
 pysb>=1.11.0
+jax>=0.4.26
+diffrax>=0.5.0
 matplotlib==3.7.1
 nbsphinx==0.9.1
 nbformat==5.8.0
diff --git a/include/amici/hdf5.h b/include/amici/hdf5.h
index 32cd4c925a..cbb5d93f74 100644
--- a/include/amici/hdf5.h
+++ b/include/amici/hdf5.h
@@ -26,6 +26,7 @@ class ReturnData;
 class ExpData;
 class Model;
 class Solver;
+struct LogItem;
 
 namespace hdf5 {
 
@@ -137,6 +138,17 @@ void writeReturnDataDiagnosis(
     std::string const& hdf5Location
 );
 
+/**
+ * @brief Write log message to HDF5 file
+ * @param file HDF5 file to write to
+ * @param logItems Log items to write
+ * @param hdf5Location Full dataset path inside the HDF5 file (will be created)
+ */
+void writeLogItemsToHDF5(
+    H5::H5File const& file, std::vector<amici::LogItem> const& logItems,
+    std::string const& hdf5Location
+);
+
 /**
  * @brief Create the given group and possibly parents.
  * @param file HDF5 file to write to
@@ -164,8 +176,8 @@ std::unique_ptr<ExpData> readSimulationExpData(
 /**
  * @brief Write AMICI experimental data to HDF5 file.
  * @param edata The experimental data which is to be written
- * @param file Name of HDF5 file
- * @param hdf5Location Path inside the HDF5 file to object having ExpData
+ * @param file HDF5 file
+ * @param hdf5Location Path inside the HDF5 file
  */
 
 void writeSimulationExpData(
@@ -173,6 +185,17 @@ void writeSimulationExpData(
     std::string const& hdf5Location
 );
 
+/**
+ * @brief Write AMICI experimental data to HDF5 file.
+ * @param edata The experimental data which is to be written
+ * @param file Name of HDF5 file
+ * @param hdf5Location Path inside the HDF5 file
+ */
+void writeSimulationExpData(
+    ExpData const& edata, std::string const& hdf5Filename,
+    std::string const& hdf5Location
+);
+
 /**
  * @brief Check whether an attribute with the given name exists
  * on the given dataset.
diff --git a/include/amici/rdata.h b/include/amici/rdata.h
index df25f39923..793be9435a 100644
--- a/include/amici/rdata.h
+++ b/include/amici/rdata.h
@@ -453,7 +453,7 @@ class ReturnData : public ModelDimensions {
 
     /** boolean indicating whether residuals for standard deviations have been
      * added */
-    bool sigma_res;
+    bool sigma_res{false};
 
     /** log messages */
     std::vector<LogItem> messages;
@@ -463,7 +463,7 @@ class ReturnData : public ModelDimensions {
 
   protected:
     /** offset for sigma_residuals */
-    realtype sigma_offset;
+    realtype sigma_offset{0.0};
 
     /** array of number of found roots for a certain event type
      * (shape `ne`) */
diff --git a/include/amici/solver.h b/include/amici/solver.h
index 5aa2d7830e..84eb479cf6 100644
--- a/include/amici/solver.h
+++ b/include/amici/solver.h
@@ -39,7 +39,8 @@ namespace amici {
  * variables and status flags) are specified as mutable and not included in
  * serialization or equality checks. No solver setting parameter should be
  * marked mutable.
- *
+ */
+/*
  * NOTE: Any changes in data members here must be propagated to copy ctor,
  * equality operator, serialization functions in serialization.h, and
  * amici::hdf5::(read/write)SolverSettings(From/To)HDF5 in hdf5.cpp.
@@ -1868,10 +1869,6 @@ class Solver {
     /** maximum number of allowed Newton steps for steady state computation */
     long int newton_maxsteps_{0L};
 
-    /** maximum number of allowed linear steps per Newton step for steady state
-     * computation */
-    long int newton_maxlinsteps_{0L};
-
     /** Damping factor state used int the Newton method */
     NewtonDampingFactorMode newton_damping_factor_mode_{
         NewtonDampingFactorMode::on
diff --git a/python/examples/example_jax_petab/ExampleJaxPEtab.ipynb b/python/examples/example_jax_petab/ExampleJaxPEtab.ipynb
new file mode 100644
index 0000000000..10369f74b0
--- /dev/null
+++ b/python/examples/example_jax_petab/ExampleJaxPEtab.ipynb
@@ -0,0 +1,1162 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d4d2bc5c",
+   "metadata": {},
+   "source": [
+    "# Simulating AMICI models using JAX\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "This guide demonstrates how to use AMICI to export models in a format compatible with the [JAX](https://jax.readthedocs.io/en/latest/) ecosystem, enabling simulations with the [diffrax](https://docs.kidger.site/diffrax/) library. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb2fe897",
+   "metadata": {},
+   "source": [
+    "## Preparation\n",
+    "\n",
+    "To begin, we will import a model using [PEtab](https://petab.readthedocs.io). For this demonstration, we will utilize the [Benchmark Collection](https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab), which provides a diverse set of models. For more information on importing PEtab models, refer to the corresponding [PEtab notebook](https://amici.readthedocs.io/en/latest/petab.html).\n",
+    "\n",
+    "In this tutorial, we will import the Böhm model from the Benchmark Collection. Using [amici.petab_import](https://amici.readthedocs.io/en/latest/generated/amici.petab_import.html#amici.petab_import.import_petab_problem), we will load the PEtab problem. To create a  [JAXModel](https://amici.readthedocs.io/en/latest/generated/amici.jax.html#amici.jax.JAXModel) instead of a standard AMICI model, we set the `jax` parameter to `True`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6ada3fb8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:50:53.712145Z",
+     "start_time": "2024-11-19T09:50:47.191184Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from amici.petab.petab_import import import_petab_problem\n",
+    "import petab.v1 as petab\n",
+    "\n",
+    "# Define the model name and YAML file location\n",
+    "model_name = \"Boehm_JProteomeRes2014\"\n",
+    "yaml_url = (\n",
+    "    f\"https://raw.githubusercontent.com/Benchmarking-Initiative/Benchmark-Models-PEtab/\"\n",
+    "    f\"master/Benchmark-Models/{model_name}/{model_name}.yaml\"\n",
+    ")\n",
+    "\n",
+    "# Load the PEtab problem from the YAML file\n",
+    "petab_problem = petab.Problem.from_yaml(yaml_url)\n",
+    "\n",
+    "# Import the PEtab problem as a JAX-compatible AMICI model\n",
+    "jax_model = import_petab_problem(\n",
+    "    petab_problem,\n",
+    "    compile_=True,  # do not compile regular amici model\n",
+    "    verbose=False,  # no text output\n",
+    "    jax=True,  # return jax model\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5258566d99c89ba4",
+   "metadata": {},
+   "source": [
+    "## Simulation\n",
+    "\n",
+    "In principle, we can already use this model for simulation using the [simulate_condition](https://amici.readthedocs.io/en/latest/generated/amici.jax.html#amici.jax.JAXModel.simulate_condition) method. However, this approach can be cumbersome as timepoints, data etc. need to be specified manually. Instead, we process the PEtab problem into a [JAXProblem](https://amici.readthedocs.io/en/latest/generated/amici.jax.html#amici.jax.JAXProblem), which enables efficient simulation using [amici.jax.run_simulations]((https://amici.readthedocs.io/en/latest/generated/amici.jax.html#amici.jax.run_simulations)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "76c1331372cd51b4",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:50:56.042924Z",
+     "start_time": "2024-11-19T09:50:53.718372Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from amici.jax import JAXProblem, run_simulations\n",
+    "\n",
+    "# Create a JAXProblem from the JAX model and PEtab problem\n",
+    "jax_problem = JAXProblem(jax_model, petab_problem)\n",
+    "\n",
+    "# Run simulations and compute the log-likelihood\n",
+    "llh, results = run_simulations(jax_problem)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f8684d76368bd76",
+   "metadata": {},
+   "source": "This simulates the model for all conditions using the nominal parameter values. Simple, right? Now, let’s take a look at the simulation results."
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2fc284bd3bfb3a62",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:50:56.141898Z",
+     "start_time": "2024-11-19T09:50:56.134945Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Array(nan, dtype=float32),\n",
+       " {'stats_dyn': {'max_steps': 1024,\n",
+       "   'num_accepted_steps': Array(778, dtype=int32, weak_type=True),\n",
+       "   'num_rejected_steps': Array(246, dtype=int32, weak_type=True),\n",
+       "   'num_steps': Array(1024, dtype=int32, weak_type=True)},\n",
+       "  'stats_posteq': None,\n",
+       "  'stats_preeq': None,\n",
+       "  'ts': Array([  0. ,   0. ,   0. ,   2.5,   2.5,   2.5,   5. ,   5. ,   5. ,\n",
+       "          10. ,  10. ,  10. ,  15. ,  15. ,  15. ,  20. ,  20. ,  20. ,\n",
+       "          30. ,  30. ,  30. ,  40. ,  40. ,  40. ,  50. ,  50. ,  50. ,\n",
+       "          60. ,  60. ,  60. ,  80. ,  80. ,  80. , 100. , 100. , 100. ,\n",
+       "         120. , 120. , 120. , 160. , 160. , 160. , 200. , 200. , 200. ,\n",
+       "         240. , 240. , 240. ], dtype=float32),\n",
+       "  'x': Array([[143.8668,  63.7332,   0.    ,   0.    ,   0.    ,   0.    ,\n",
+       "            0.    ,   0.    ],\n",
+       "         [143.8668,  63.7332,   0.    ,   0.    ,   0.    ,   0.    ,\n",
+       "            0.    ,   0.    ],\n",
+       "         [143.8668,  63.7332,   0.    ,   0.    ,   0.    ,   0.    ,\n",
+       "            0.    ,   0.    ],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf],\n",
+       "         [     inf,      inf,      inf,      inf,      inf,      inf,\n",
+       "               inf,      inf]], dtype=float32)})"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define the simulation condition\n",
+    "simulation_condition = (\"model1_data1\",)\n",
+    "\n",
+    "# Access the results for the specified condition\n",
+    "results[simulation_condition]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa46125e508d38d3",
+   "metadata": {},
+   "source": [
+    "Unfortunately, the simulation failed! As seen in the output, the simulation broke down after the initial timepoint, indicated by the `inf` values in the state variables `results[simulation_condition][1].x` and the `nan` likelihood value. A closer inspection of this variable provides additional clues about what might have gone wrong.\n",
+    "\n",
+    "The issue stems from using single precision, as indicated by the `float32` dtype of state variables. Single precision is generally a [bad idea](https://docs.kidger.site/diffrax/examples/stiff_ode/) for stiff systems like the Böhm model. Let’s retry the simulation with double precision."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8e5006774534ba3a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:50:58.227222Z",
+     "start_time": "2024-11-19T09:50:56.235939Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{('model1_data1',): (Array(-138.22199834, dtype=float64),\n",
+       "  {'stats_dyn': {'max_steps': 1024,\n",
+       "    'num_accepted_steps': Array(125, dtype=int64, weak_type=True),\n",
+       "    'num_rejected_steps': Array(7, dtype=int64, weak_type=True),\n",
+       "    'num_steps': Array(132, dtype=int64, weak_type=True)},\n",
+       "   'stats_posteq': None,\n",
+       "   'stats_preeq': None,\n",
+       "   'ts': Array([  0. ,   0. ,   0. ,   2.5,   2.5,   2.5,   5. ,   5. ,   5. ,\n",
+       "           10. ,  10. ,  10. ,  15. ,  15. ,  15. ,  20. ,  20. ,  20. ,\n",
+       "           30. ,  30. ,  30. ,  40. ,  40. ,  40. ,  50. ,  50. ,  50. ,\n",
+       "           60. ,  60. ,  60. ,  80. ,  80. ,  80. , 100. , 100. , 100. ,\n",
+       "          120. , 120. , 120. , 160. , 160. , 160. , 200. , 200. , 200. ,\n",
+       "          240. , 240. , 240. ], dtype=float64),\n",
+       "   'x': Array([[1.43866806e+02, 6.37332001e+01, 0.00000000e+00, 0.00000000e+00,\n",
+       "           0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "          [1.43866806e+02, 6.37332001e+01, 0.00000000e+00, 0.00000000e+00,\n",
+       "           0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "          [1.43866806e+02, 6.37332001e+01, 0.00000000e+00, 0.00000000e+00,\n",
+       "           0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "          [5.34614747e+01, 2.88662915e+01, 1.73038463e+01, 5.38666098e-05,\n",
+       "           1.57043241e-05, 1.12989551e+02, 1.44740461e+00, 2.65965680e+01],\n",
+       "          [5.34614747e+01, 2.88662915e+01, 1.73038463e+01, 5.38666098e-05,\n",
+       "           1.57043241e-05, 1.12989551e+02, 1.44740461e+00, 2.65965680e+01],\n",
+       "          [5.34614747e+01, 2.88662915e+01, 1.73038463e+01, 5.38666098e-05,\n",
+       "           1.57043241e-05, 1.12989551e+02, 1.44740461e+00, 2.65965680e+01],\n",
+       "          [3.40645243e+01, 1.96396741e+01, 2.10101056e+01, 2.04431389e-05,\n",
+       "           6.79533169e-06, 1.36155797e+02, 3.93060446e+00, 3.39422194e+01],\n",
+       "          [3.40645243e+01, 1.96396741e+01, 2.10101056e+01, 2.04431389e-05,\n",
+       "           6.79533169e-06, 1.36155797e+02, 3.93060446e+00, 3.39422194e+01],\n",
+       "          [3.40645243e+01, 1.96396741e+01, 2.10101056e+01, 2.04431389e-05,\n",
+       "           6.79533169e-06, 1.36155797e+02, 3.93060446e+00, 3.39422194e+01],\n",
+       "          [2.17740069e+01, 1.28936829e+01, 2.26400305e+01, 7.29828626e-06,\n",
+       "           2.55916689e-06, 1.49922977e+02, 9.56261350e+00, 3.90845534e+01],\n",
+       "          [2.17740069e+01, 1.28936829e+01, 2.26400305e+01, 7.29828626e-06,\n",
+       "           2.55916689e-06, 1.49922977e+02, 9.56261350e+00, 3.90845534e+01],\n",
+       "          [2.17740069e+01, 1.28936829e+01, 2.26400305e+01, 7.29828626e-06,\n",
+       "           2.55916689e-06, 1.49922977e+02, 9.56261350e+00, 3.90845534e+01],\n",
+       "          [1.78289538e+01, 1.02603483e+01, 2.23703281e+01, 4.27571773e-06,\n",
+       "           1.41605997e-06, 1.53605377e+02, 1.53104054e+01, 4.07264964e+01],\n",
+       "          [1.78289538e+01, 1.02603483e+01, 2.23703281e+01, 4.27571773e-06,\n",
+       "           1.41605997e-06, 1.53605377e+02, 1.53104054e+01, 4.07264964e+01],\n",
+       "          [1.78289538e+01, 1.02603483e+01, 2.23703281e+01, 4.27571773e-06,\n",
+       "           1.41605997e-06, 1.53605377e+02, 1.53104054e+01, 4.07264964e+01],\n",
+       "          [1.63397301e+01, 8.95194886e+00, 2.15687556e+01, 3.13802765e-06,\n",
+       "           9.41897178e-07, 1.54369347e+02, 2.09093940e+01, 4.12091821e+01],\n",
+       "          [1.63397301e+01, 8.95194886e+00, 2.15687556e+01, 3.13802765e-06,\n",
+       "           9.41897178e-07, 1.54369347e+02, 2.09093940e+01, 4.12091821e+01],\n",
+       "          [1.63397301e+01, 8.95194886e+00, 2.15687556e+01, 3.13802765e-06,\n",
+       "           9.41897178e-07, 1.54369347e+02, 2.09093940e+01, 4.12091821e+01],\n",
+       "          [1.59598663e+01, 7.84978463e+00, 1.95400559e+01, 2.28580865e-06,\n",
+       "           5.52965361e-07, 1.52878988e+02, 3.13834269e+01, 4.08423997e+01],\n",
+       "          [1.59598663e+01, 7.84978463e+00, 1.95400559e+01, 2.28580865e-06,\n",
+       "           5.52965361e-07, 1.52878988e+02, 3.13834269e+01, 4.08423997e+01],\n",
+       "          [1.59598663e+01, 7.84978463e+00, 1.95400559e+01, 2.28580865e-06,\n",
+       "           5.52965361e-07, 1.52878988e+02, 3.13834269e+01, 4.08423997e+01],\n",
+       "          [1.68960409e+01, 7.57954992e+00, 1.74766781e+01, 1.95598628e-06,\n",
+       "           3.93623013e-07, 1.49923893e+02, 4.08004734e+01, 3.97639408e+01],\n",
+       "          [1.68960409e+01, 7.57954992e+00, 1.74766781e+01, 1.95598628e-06,\n",
+       "           3.93623013e-07, 1.49923893e+02, 4.08004734e+01, 3.97639408e+01],\n",
+       "          [1.68960409e+01, 7.57954992e+00, 1.74766781e+01, 1.95598628e-06,\n",
+       "           3.93623013e-07, 1.49923893e+02, 4.08004734e+01, 3.97639408e+01],\n",
+       "          [1.83667585e+01, 7.66955396e+00, 1.55594015e+01, 1.76473276e-06,\n",
+       "           3.07719966e-07, 1.46418868e+02, 4.91998176e+01, 3.84066930e+01],\n",
+       "          [1.83667585e+01, 7.66955396e+00, 1.55594015e+01, 1.76473276e-06,\n",
+       "           3.07719966e-07, 1.46418868e+02, 4.91998176e+01, 3.84066930e+01],\n",
+       "          [1.83667585e+01, 7.66955396e+00, 1.55594015e+01, 1.76473276e-06,\n",
+       "           3.07719966e-07, 1.46418868e+02, 4.91998176e+01, 3.84066930e+01],\n",
+       "          [2.01288255e+01, 7.95104827e+00, 1.38272785e+01, 1.61833093e-06,\n",
+       "           2.52512177e-07, 1.42637837e+02, 5.66687226e+01, 3.69287741e+01],\n",
+       "          [2.01288255e+01, 7.95104827e+00, 1.38272785e+01, 1.61833093e-06,\n",
+       "           2.52512177e-07, 1.42637837e+02, 5.66687226e+01, 3.69287741e+01],\n",
+       "          [2.01288255e+01, 7.95104827e+00, 1.38272785e+01, 1.61833093e-06,\n",
+       "           2.52512177e-07, 1.42637837e+02, 5.66687226e+01, 3.69287741e+01],\n",
+       "          [2.42069672e+01, 8.82343809e+00, 1.09015504e+01, 1.36440625e-06,\n",
+       "           1.81275253e-07, 1.34584160e+02, 6.91907904e+01, 3.38618223e+01],\n",
+       "          [2.42069672e+01, 8.82343809e+00, 1.09015504e+01, 1.36440625e-06,\n",
+       "           1.81275253e-07, 1.34584160e+02, 6.91907904e+01, 3.38618223e+01],\n",
+       "          [2.42069672e+01, 8.82343809e+00, 1.09015504e+01, 1.36440625e-06,\n",
+       "           1.81275253e-07, 1.34584160e+02, 6.91907904e+01, 3.38618223e+01],\n",
+       "          [2.88236929e+01, 9.92100237e+00, 8.58815552e+00, 1.12770626e-06,\n",
+       "           1.33599425e-07, 1.26069389e+02, 7.90544164e+01, 3.08213014e+01],\n",
+       "          [2.88236929e+01, 9.92100237e+00, 8.58815552e+00, 1.12770626e-06,\n",
+       "           1.33599425e-07, 1.26069389e+02, 7.90544164e+01, 3.08213014e+01],\n",
+       "          [2.88236929e+01, 9.92100237e+00, 8.58815552e+00, 1.12770626e-06,\n",
+       "           1.33599425e-07, 1.26069389e+02, 7.90544164e+01, 3.08213014e+01],\n",
+       "          [3.38427746e+01, 1.11365012e+01, 6.75633027e+00, 9.06279023e-07,\n",
+       "           9.81352036e-08, 1.17230823e+02, 8.68156402e+01, 2.78994196e+01],\n",
+       "          [3.38427746e+01, 1.11365012e+01, 6.75633027e+00, 9.06279023e-07,\n",
+       "           9.81352036e-08, 1.17230823e+02, 8.68156402e+01, 2.78994196e+01],\n",
+       "          [3.38427746e+01, 1.11365012e+01, 6.75633027e+00, 9.06279023e-07,\n",
+       "           9.81352036e-08, 1.17230823e+02, 8.68156402e+01, 2.78994196e+01],\n",
+       "          [4.45767678e+01, 1.36929100e+01, 4.13936161e+00, 5.34332520e-07,\n",
+       "           5.04178629e-08, 9.91750041e+01, 9.76743159e+01, 2.25642862e+01],\n",
+       "          [4.45767678e+01, 1.36929100e+01, 4.13936161e+00, 5.34332520e-07,\n",
+       "           5.04178629e-08, 9.91750041e+01, 9.76743159e+01, 2.25642862e+01],\n",
+       "          [4.45767678e+01, 1.36929100e+01, 4.13936161e+00, 5.34332520e-07,\n",
+       "           5.04178629e-08, 9.91750041e+01, 9.76743159e+01, 2.25642862e+01],\n",
+       "          [5.53512751e+01, 1.61684905e+01, 2.47997315e+00, 2.79973425e-07,\n",
+       "           2.38894456e-08, 8.17101310e+01, 1.04245916e+02, 1.80088542e+01],\n",
+       "          [5.53512751e+01, 1.61684905e+01, 2.47997315e+00, 2.79973425e-07,\n",
+       "           2.38894456e-08, 8.17101310e+01, 1.04245916e+02, 1.80088542e+01],\n",
+       "          [5.53512751e+01, 1.61684905e+01, 2.47997315e+00, 2.79973425e-07,\n",
+       "           2.38894456e-08, 8.17101310e+01, 1.04245916e+02, 1.80088542e+01],\n",
+       "          [6.52754860e+01, 1.83796881e+01, 1.44531833e+00, 1.32320205e-07,\n",
+       "           1.04906457e-08, 6.59469727e+01, 1.08115837e+02, 1.42437160e+01],\n",
+       "          [6.52754860e+01, 1.83796881e+01, 1.44531833e+00, 1.32320205e-07,\n",
+       "           1.04906457e-08, 6.59469727e+01, 1.08115837e+02, 1.42437160e+01],\n",
+       "          [6.52754860e+01, 1.83796881e+01, 1.44531833e+00, 1.32320205e-07,\n",
+       "           1.04906457e-08, 6.59469727e+01, 1.08115837e+02, 1.42437160e+01]],      dtype=float64)})}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import jax\n",
+    "\n",
+    "# Enable double precision in JAX\n",
+    "jax.config.update(\"jax_enable_x64\", True)\n",
+    "\n",
+    "# Re-run simulations with double precision\n",
+    "llh, results = run_simulations(jax_problem)\n",
+    "\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fea37568206351f7",
+   "metadata": {},
+   "source": "Success! The simulation completed successfully, and we can now plot the resulting state trajectories."
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "95c75d098d3a1822",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:50:58.490052Z",
+     "start_time": "2024-11-19T09:50:58.305876Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "def plot_simulation(results):\n",
+    "    \"\"\"\n",
+    "    Plot the state trajectories from the simulation results.\n",
+    "\n",
+    "    Parameters:\n",
+    "        results (dict): Simulation results from run_simulations.\n",
+    "    \"\"\"\n",
+    "    # Extract the simulation results for the specific condition\n",
+    "    sim_results = results[simulation_condition][1]\n",
+    "\n",
+    "    # Create a new figure for the state trajectories\n",
+    "    plt.figure(figsize=(8, 6))\n",
+    "    for idx in range(sim_results[\"x\"].shape[1]):\n",
+    "        time_points = np.array(sim_results[\"ts\"])\n",
+    "        state_values = np.array(sim_results[\"x\"][:, idx])\n",
+    "        plt.plot(time_points, state_values, label=jax_model.state_ids[idx])\n",
+    "\n",
+    "    # Add labels, legend, and grid\n",
+    "    plt.xlabel(\"Time\")\n",
+    "    plt.ylabel(\"State Values\")\n",
+    "    plt.title(simulation_condition)\n",
+    "    plt.legend()\n",
+    "    plt.grid(True)\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "# Plot the simulation results\n",
+    "plot_simulation(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f57c07211b781ab5",
+   "metadata": {},
+   "source": "`run_simulations` enables users to specify the simulation conditions to be executed. For more complex models, this allows for restricting simulations to a subset of conditions. Since the Böhm model includes only a single condition, we demonstrate this functionality by simulating no condition at all."
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2f2e1c7023ad261b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:50:58.505973Z",
+     "start_time": "2024-11-19T09:50:58.501775Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llh, results = run_simulations(jax_problem, simulation_conditions=tuple())\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b729e1b-3c75-4a87-a33b-0a54622609e7",
+   "metadata": {},
+   "source": [
+    "## Updating Parameters\n",
+    "\n",
+    "As next step, we will update the parameter values used for simulation. However, if we attempt to directly modify the values in `JAXModel.parameters`, we encounter a `FrozenInstanceError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "75df1ab9e8a738a0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:50:58.685750Z",
+     "start_time": "2024-11-19T09:50:58.575034Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error: cannot assign to field 'parameters'\n"
+     ]
+    }
+   ],
+   "source": [
+    "from dataclasses import FrozenInstanceError\n",
+    "import jax\n",
+    "\n",
+    "# Generate random noise to update the parameters\n",
+    "noise = (\n",
+    "    jax.random.normal(\n",
+    "        key=jax.random.PRNGKey(0), shape=jax_problem.parameters.shape\n",
+    "    )\n",
+    "    / 10\n",
+    ")\n",
+    "\n",
+    "# Attempt to update the parameters\n",
+    "try:\n",
+    "    jax_problem.parameters += noise\n",
+    "except FrozenInstanceError as e:\n",
+    "    print(\"Error:\", e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b91941cf707704c3",
+   "metadata": {},
+   "source": [
+    "The root cause of this error lies in the fact that, to enable autodiff, direct modifications of attributes are not allowed in [equinox](https://docs.kidger.site/equinox/), which AMICI utilizes under the hood. Consequently, attributes of instances like `JAXModel` or `JAXProblem` cannot be updated directly — this is the price we have to pay for autodiff.\n",
+    "\n",
+    "However, `JAXProblem` provides a convenient method called [update_parameters](https://amici.readthedocs.io/en/latest/generated/amici.jax.html#amici.jax.JAXProblem.update_parameters). The caveat is that this method creates a new JAXProblem instance instead of modifying the existing one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "feb125b6-4f84-427c-b870-421a328eee81",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:51:00.631866Z",
+     "start_time": "2024-11-19T09:50:58.702698Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Update the parameters and create a new JAXProblem instance\n",
+    "jax_problem = jax_problem.update_parameters(jax_problem.parameters + noise)\n",
+    "\n",
+    "# Run simulations with the updated parameters\n",
+    "llh, results = run_simulations(jax_problem)\n",
+    "\n",
+    "# Plot the simulation results\n",
+    "plot_simulation(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e73bdd447a4d48c8",
+   "metadata": {},
+   "source": [
+    "## Computing Gradients\n",
+    "\n",
+    "Similar to updating attributes, computing gradients in the JAX ecosystem can feel a bit unconventional if you’re not familiar with the JAX ecosysmt. JAX offers [powerful automatic differentiation](https://jax.readthedocs.io/en/latest/automatic-differentiation.html) through the `jax.grad` function. However, to use `jax.grad` with `JAXProblem`, we need to specify which parts of the `JAXProblem` should be treated as static."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a8918f59607e6525",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:51:00.662578Z",
+     "start_time": "2024-11-19T09:51:00.649386Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error: Argument 'ParameterMappingForCondition(map_sim_var={'Epo_degradation_BaF3': 'Epo_degradation_BaF3', 'k_exp_hetero': 'k_exp_hetero', 'k_exp_homo': 'k_exp_homo', 'k_imp_hetero': 'k_imp_hetero', 'k_imp_homo': 'k_imp_homo', 'k_phos': 'k_phos', 'ratio': 0.693, 'specC17': 0.107, 'noiseParameter1_pSTAT5A_rel': 'sd_pSTAT5A_rel', 'noiseParameter1_pSTAT5B_rel': 'sd_pSTAT5B_rel', 'noiseParameter1_rSTAT5A_rel': 'sd_rSTAT5A_rel'},scale_map_sim_var={'Epo_degradation_BaF3': 'log10', 'k_exp_hetero': 'log10', 'k_exp_homo': 'log10', 'k_imp_hetero': 'log10', 'k_imp_homo': 'log10', 'k_phos': 'log10', 'ratio': 'lin', 'specC17': 'lin', 'noiseParameter1_pSTAT5A_rel': 'log10', 'noiseParameter1_pSTAT5B_rel': 'log10', 'noiseParameter1_rSTAT5A_rel': 'log10'},map_preeq_fix={},scale_map_preeq_fix={},map_sim_fix={},scale_map_sim_fix={})' of type <class 'amici.petab.parameter_mapping.ParameterMappingForCondition'> is not a valid JAX type.\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    # Attempt to compute the gradient of the run_simulations function\n",
+    "    jax.grad(run_simulations, has_aux=True)(jax_problem)\n",
+    "except TypeError as e:\n",
+    "    print(\"Error:\", e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "922a9ffd94c99607",
+   "metadata": {},
+   "source": "Fortunately, `equinox` simplifies this process by offering [filter_grad](https://docs.kidger.site/equinox/api/transformations/#equinox.filter_grad), which enables autodiff functionality that is compatible with `JAXProblem` and, in theory, also with `JAXModel`."
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e2c635b6-79db-4e78-8738-789af29110b5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:51:07.293314Z",
+     "start_time": "2024-11-19T09:51:00.709141Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import equinox as eqx\n",
+    "\n",
+    "# Compute the gradient using equinox's filter_grad, preserving auxiliary outputs\n",
+    "grad, _ = eqx.filter_grad(run_simulations, has_aux=True)(jax_problem)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fd639ad39948e72",
+   "metadata": {},
+   "source": "Functions transformed by `filter_grad` return gradients that share the same structure as the first argument (unless specified otherwise). This allows us to access the gradient with respect to the parameters attribute directly `via grad.parameters`."
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ab9225bf704e9ed5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:51:07.310244Z",
+     "start_time": "2024-11-19T09:51:07.306293Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Array([ 2.39759630e+01, -1.36704159e-01,  1.33625245e+01,  3.25229304e+01,\n",
+       "        4.88660333e-05,  5.39482681e+01, -5.13624151e+00, -2.90885864e-02,\n",
+       "        6.08639536e+01], dtype=float64)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grad.parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5793acc4ad8908be",
+   "metadata": {},
+   "source": "Attributes for which derivatives cannot be computed (typically anything that is not a [jax.numpy.array](https://jax.readthedocs.io/en/latest/_autosummary/jax.numpy.array.html)) are automatically set to `None`."
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "77e6bc4fa3e6970a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:51:07.398319Z",
+     "start_time": "2024-11-19T09:51:07.392032Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "JAXProblem(\n",
+       "  parameters=f64[9],\n",
+       "  model=JAXModel_Boehm_JProteomeRes2014(api_version='0.0.1'),\n",
+       "  _parameter_mappings={'model1_data1': None},\n",
+       "  _measurements={('model1_data1',): (f64[3], f64[45], f64[0], f64[48], None)},\n",
+       "  _petab_problem=None\n",
+       ")"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grad"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75fc08817f1b4734",
+   "metadata": {},
+   "source": "Observant readers may notice that the gradient above appears to include numeric values for derivatives with respect to some measurements. However, `simulation_conditions` internally disables gradient computations using `jax.lax.stop_gradient`, resulting in these values being zeroed out."
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a8b7634e-7bd8-41ae-a6dc-1d0f29993ac0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:51:07.455764Z",
+     "start_time": "2024-11-19T09:51:07.450233Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Array([0., 0., 0.], dtype=float64),\n",
+       " Array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float64),\n",
+       " Array([], shape=(0,), dtype=float64),\n",
+       " Array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],      dtype=float64),\n",
+       " None)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grad._measurements[simulation_condition]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c6c4f2d3a2673a2",
+   "metadata": {},
+   "source": "However, we can compute derivatives with respect to data elements using `JAXModel.simulate_condition`. In the example below, we differentiate the observables `y` (specified by passing `y` to the `ret` argument) with respect to the timepoints at which the model outputs are computed after the solving the differential equation. While this might not be particularly practical, it serves as an nice illustration of the power of automatic differentiation."
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2a843410-4af4-4ff7-8b67-9293a5820caf",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:51:13.735937Z",
+     "start_time": "2024-11-19T09:51:07.494491Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Array([[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
+       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
+       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
+       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
+       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
+       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
+       "       ...,\n",
+       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
+       "        -1.30871686e-01,  0.00000000e+00, -3.80465095e-11],\n",
+       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
+       "         0.00000000e+00, -2.69250222e-01, -7.93596886e-11],\n",
+       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
+       "         0.00000000e+00,  0.00000000e+00, -2.29968854e-02]],      dtype=float64)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import jax.numpy as jnp\n",
+    "import diffrax\n",
+    "\n",
+    "# Define the simulation condition\n",
+    "simulation_condition = (\"model1_data1\",)\n",
+    "\n",
+    "# Load condition-specific data\n",
+    "ts_preeq, ts_dyn, ts_posteq, my, iys = jax_problem._measurements[\n",
+    "    simulation_condition\n",
+    "]\n",
+    "\n",
+    "# Load parameters for the specified condition\n",
+    "p = jax_problem.load_parameters(simulation_condition[0])\n",
+    "# Disable preequilibration\n",
+    "p_preeq = jnp.array([])\n",
+    "\n",
+    "\n",
+    "# Define a function to compute the gradient with respect to dynamic timepoints\n",
+    "@eqx.filter_jacfwd\n",
+    "def grad_ts_dyn(tt):\n",
+    "    return jax_problem.model.simulate_condition(\n",
+    "        p=p,\n",
+    "        p_preeq=p_preeq,\n",
+    "        ts_preeq=ts_preeq,\n",
+    "        ts_dyn=tt,\n",
+    "        ts_posteq=ts_posteq,\n",
+    "        my=jnp.array(my),\n",
+    "        iys=jnp.array(iys),\n",
+    "        solver=diffrax.Kvaerno5(),\n",
+    "        controller=diffrax.PIDController(atol=1e-8, rtol=1e-8),\n",
+    "        max_steps=2**10,\n",
+    "        adjoint=diffrax.DirectAdjoint(),\n",
+    "        ret=\"y\",  # Return observables\n",
+    "    )[0]\n",
+    "\n",
+    "\n",
+    "# Compute the gradient with respect to `ts_dyn`\n",
+    "g = grad_ts_dyn(ts_dyn)\n",
+    "g"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9cec2a77b30669d",
+   "metadata": {},
+   "source": [
+    "## Compilation & Profiling\n",
+    "\n",
+    "To maximize performance with JAX, code should be just-in-time (JIT) compiled. This can be achieved using the `jax.jit` or `equinox.filter_jit` decorators. While JIT compilation introduces some overhead during the first function call, it significantly improves performance for subsequent calls. To demonstrate this, we will first clear the JIT cache and then profile the execution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d1f79c45ab2eccdc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:51:14.292251Z",
+     "start_time": "2024-11-19T09:51:13.834276Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from time import time\n",
+    "\n",
+    "# Clear JAX caches to ensure a fresh start\n",
+    "jax.clear_caches()\n",
+    "\n",
+    "# Define a JIT-compiled gradient function with auxiliary outputs\n",
+    "gradfun = eqx.filter_jit(eqx.filter_grad(run_simulations, has_aux=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b44881332070e2b0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:51:23.060962Z",
+     "start_time": "2024-11-19T09:51:14.309832Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Function compilation time: 2.53 seconds\n",
+      "Gradient compilation time: 6.21 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Measure the time taken for the first function call (including compilation)\n",
+    "start = time()\n",
+    "run_simulations(jax_problem)\n",
+    "print(f\"Function compilation time: {time() - start:.2f} seconds\")\n",
+    "\n",
+    "# Measure the time taken for the gradient computation (including compilation)\n",
+    "start = time()\n",
+    "gradfun(jax_problem)\n",
+    "print(f\"Gradient compilation time: {time() - start:.2f} seconds\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "a3e1463209074861",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:51:25.374277Z",
+     "start_time": "2024-11-19T09:51:23.078334Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16.6 ms ± 609 μs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "run_simulations(\n",
+    "    jax_problem,\n",
+    "    controller=diffrax.PIDController(\n",
+    "        rtol=1e-8,  # same as amici default\n",
+    "        atol=1e-16,  # same as amici default\n",
+    "        pcoeff=0.4,  # recommended value for stiff systems\n",
+    "        icoeff=0.3,  # recommended value for stiff systems\n",
+    "        dcoeff=0.0,  # recommended value for stiff systems\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2f074fbbebf834c6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:51:31.394645Z",
+     "start_time": "2024-11-19T09:51:25.459759Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "39.8 ms ± 854 μs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit \n",
+    "gradfun(\n",
+    "    jax_problem,\n",
+    "    controller=diffrax.PIDController(\n",
+    "        rtol=1e-8,  # same as amici default\n",
+    "        atol=1e-16,  # same as amici default\n",
+    "        pcoeff=0.4,  # recommended value for stiff systems\n",
+    "        icoeff=0.3,  # recommended value for stiff systems\n",
+    "        dcoeff=0.0,  # recommended value for stiff systems\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "5f68c5fcc16b637",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:51:55.244925Z",
+     "start_time": "2024-11-19T09:51:31.477484Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from amici.petab import simulate_petab\n",
+    "import amici\n",
+    "\n",
+    "# Import the PEtab problem as a standard AMICI model\n",
+    "amici_model = import_petab_problem(\n",
+    "    petab_problem,\n",
+    "    compile_=False,  # do not recompile\n",
+    "    verbose=False,\n",
+    "    jax=False,  # load the amici model this time\n",
+    ")\n",
+    "\n",
+    "# Configure the solver with appropriate tolerances\n",
+    "solver = amici_model.getSolver()\n",
+    "solver.setAbsoluteTolerance(1e-8)\n",
+    "solver.setRelativeTolerance(1e-8)\n",
+    "\n",
+    "# Prepare the parameters for the simulation\n",
+    "problem_parameters = dict(\n",
+    "    zip(jax_problem.parameter_ids, jax_problem.parameters)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "413ed7c60b2cf4be",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:51:55.259985Z",
+     "start_time": "2024-11-19T09:51:55.257937Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Profile simulation only\n",
+    "solver.setSensitivityOrder(amici.SensitivityOrder.none)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "768fa60e439ca8b4",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:51:57.417608Z",
+     "start_time": "2024-11-19T09:51:55.273367Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "26.1 ms ± 2.71 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit \n",
+    "simulate_petab(\n",
+    "    petab_problem,\n",
+    "    amici_model,\n",
+    "    solver=solver,\n",
+    "    problem_parameters=problem_parameters,\n",
+    "    scaled_parameters=True,\n",
+    "    scaled_gradients=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "b8382b0b2b68f49e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:51:57.497361Z",
+     "start_time": "2024-11-19T09:51:57.494502Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Profile gradient computation using forward sensitivity analysis\n",
+    "solver.setSensitivityOrder(amici.SensitivityOrder.first)\n",
+    "solver.setSensitivityMethod(amici.SensitivityMethod.forward)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "3bae1fab8c416122",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:51:59.897459Z",
+     "start_time": "2024-11-19T09:51:57.511889Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "29.1 ms ± 1.82 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit \n",
+    "simulate_petab(\n",
+    "    petab_problem,\n",
+    "    amici_model,\n",
+    "    solver=solver,\n",
+    "    problem_parameters=problem_parameters,\n",
+    "    scaled_parameters=True,\n",
+    "    scaled_gradients=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "71e0358227e1dc74",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:51:59.972149Z",
+     "start_time": "2024-11-19T09:51:59.969006Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Profile gradient computation using adjoint sensitivity analysis\n",
+    "solver.setSensitivityOrder(amici.SensitivityOrder.first)\n",
+    "solver.setSensitivityMethod(amici.SensitivityMethod.adjoint)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "e3cc7971002b6d06",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-19T09:52:03.266074Z",
+     "start_time": "2024-11-19T09:51:59.992465Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "39.3 ms ± 1.6 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit \n",
+    "simulate_petab(\n",
+    "    petab_problem,\n",
+    "    amici_model,\n",
+    "    solver=solver,\n",
+    "    problem_parameters=problem_parameters,\n",
+    "    scaled_parameters=True,\n",
+    "    scaled_gradients=True,\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/python/examples/example_splines/ExampleSplines.ipynb b/python/examples/example_splines/ExampleSplines.ipynb
index 0c237c6e6d..7958d63274 100644
--- a/python/examples/example_splines/ExampleSplines.ipynb
+++ b/python/examples/example_splines/ExampleSplines.ipynb
@@ -25,8 +25,6 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "import sys\n",
-    "from importlib import import_module\n",
     "from shutil import rmtree\n",
     "from tempfile import TemporaryDirectory\n",
     "from uuid import uuid1\n",
@@ -57,7 +55,7 @@
     "                parameters,\n",
     "                build_dir=build_dir,\n",
     "                model_name=model_name,\n",
-    "                **kwargs\n",
+    "                **kwargs,\n",
     "            )\n",
     "    else:\n",
     "        build_dir = os.path.join(BUILD_PATH, model_name)\n",
@@ -67,7 +65,7 @@
     "            parameters,\n",
     "            build_dir=build_dir,\n",
     "            model_name=model_name,\n",
-    "            **kwargs\n",
+    "            **kwargs,\n",
     "        )\n",
     "\n",
     "\n",
@@ -79,7 +77,7 @@
     "    model_name,\n",
     "    T=1,\n",
     "    discard_annotations=False,\n",
-    "    plot=True\n",
+    "    plot=True,\n",
     "):\n",
     "    if parameters is None:\n",
     "        parameters = {}\n",
@@ -89,13 +87,12 @@
     "    )\n",
     "    sbml_importer.sbml2amici(model_name, build_dir)\n",
     "    # Import the model module\n",
-    "    sys.path.insert(0, os.path.abspath(build_dir))\n",
-    "    model_module = import_module(model_name)\n",
+    "    model_module = amici.import_model_module(model_name, build_dir)\n",
     "    # Setup simulation timepoints and parameters\n",
     "    model = model_module.getModel()\n",
     "    for name, value in parameters.items():\n",
     "        model.setParameterByName(name, value)\n",
-    "    if isinstance(T, (int, float)):\n",
+    "    if isinstance(T, int | float):\n",
     "        T = np.linspace(0, T, 100)\n",
     "    model.setTimepoints([float(t) for t in T])\n",
     "    solver = model.getSolver()\n",
@@ -320,7 +317,7 @@
    ],
    "source": [
     "# Finally, we can simulate it in AMICI\n",
-    "model, rdata = simulate(sbml_model);"
+    "model, rdata = simulate(sbml_model)"
    ]
   },
   {
diff --git a/python/examples/example_steadystate/ExampleSteadystate.ipynb b/python/examples/example_steadystate/ExampleSteadystate.ipynb
index 147ae473b6..5e55114de4 100644
--- a/python/examples/example_steadystate/ExampleSteadystate.ipynb
+++ b/python/examples/example_steadystate/ExampleSteadystate.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# SBML import, observation model, sensitivity analysis, data export and visualization\n",
     "\n",
-    "This is an example using the [model_steadystate_scaled.sbml] model to demonstrate:\n",
+    "This is an example using the [model_steadystate_scaled.xml] model to demonstrate:\n",
     "\n",
     "* SBML import\n",
     "* specifying the observation model\n",
@@ -988,7 +988,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "The provided measurements can be visualized together with the simulation results by passing the `Expdata` to `amici.plotting.plot_observable_trajectories`:"
+   "source": "The provided measurements can be visualized together with the simulation results by passing the `ExpData` to `amici.plotting.plot_observable_trajectories`:"
   },
   {
    "cell_type": "code",
diff --git a/python/sdist/amici/__init__.py b/python/sdist/amici/__init__.py
index 942b669fa2..6788fefe77 100644
--- a/python/sdist/amici/__init__.py
+++ b/python/sdist/amici/__init__.py
@@ -7,12 +7,13 @@
 """
 
 import contextlib
+import importlib.util
 import importlib
 import os
 import re
 import sys
 from pathlib import Path
-from types import ModuleType as ModelModule
+from types import ModuleType
 from typing import Any
 from collections.abc import Callable
 
@@ -121,6 +122,11 @@ def _imported_from_setup() -> bool:
         assignmentRules2observables,
     )
 
+    try:
+        from .jax import JAXModel
+    except (ImportError, ModuleNotFoundError):
+        JAXModel = object
+
     @runtime_checkable
     class ModelModule(Protocol):  # noqa: F811
         """Type of AMICI-generated model modules.
@@ -135,9 +141,18 @@ def get_model(self) -> amici.Model:
             """Create a model instance."""
             ...
 
+        def get_jax_model(self) -> JAXModel: ...
+
+    AmiciModel = Union[amici.Model, amici.ModelPtr]
+else:
+    ModelModule = ModuleType
+
 
 class add_path:
-    """Context manager for temporarily changing PYTHONPATH"""
+    """Context manager for temporarily changing PYTHONPATH.
+
+    Add a path to the PYTHONPATH for the duration of the context manager.
+    """
 
     def __init__(self, path: str | Path):
         self.path: str = str(path)
@@ -151,6 +166,46 @@ def __exit__(self, exc_type, exc_value, traceback):
             sys.path.remove(self.path)
 
 
+class set_path:
+    """Context manager for temporarily changing PYTHONPATH.
+
+    Set the PYTHONPATH to a given path for the duration of the context manager.
+    """
+
+    def __init__(self, path: str | Path):
+        self.path: str = str(path)
+
+    def __enter__(self):
+        self.orginal_path = sys.path.copy()
+        sys.path = [self.path]
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        sys.path = self.orginal_path
+
+
+def _module_from_path(module_name: str, module_path: Path | str) -> ModuleType:
+    """Import a module from a given path.
+
+    Import a module from a given path. The module is not added to
+    `sys.modules`. The `_self` attribute of the module is set to the module
+    itself.
+
+    :param module_name:
+        Name of the module.
+    :param module_path:
+        Path to the module file. Absolute or relative to the current working
+        directory.
+    """
+    module_path = Path(module_path).resolve()
+    if not module_path.is_file():
+        raise ModuleNotFoundError(f"Module file not found: {module_path}")
+    spec = importlib.util.spec_from_file_location(module_name, module_path)
+    module = importlib.util.module_from_spec(spec)
+    module._self = module
+    spec.loader.exec_module(module)
+    return module
+
+
 def import_model_module(
     module_name: str, module_path: Path | str
 ) -> ModelModule:
@@ -164,34 +219,29 @@ def import_model_module(
     :return:
         The model module
     """
-    module_path = str(module_path)
+    model_root = str(module_path)
 
     # ensure we will find the newly created module
     importlib.invalidate_caches()
 
     if not os.path.isdir(module_path):
-        raise ValueError(f"module_path '{module_path}' is not a directory.")
-
-    module_path = os.path.abspath(module_path)
-
-    # module already loaded?
-    if module_name in sys.modules:
-        # if a module with that name is already in sys.modules, we remove it,
-        # along with all other modules from that package. otherwise, there
-        # will be trouble if two different models with the same name are to
-        # be imported.
-        del sys.modules[module_name]
-        # collect first, don't delete while iterating
-        to_unload = {
-            loaded_module_name
-            for loaded_module_name in sys.modules.keys()
-            if loaded_module_name.startswith(f"{module_name}.")
-        }
-        for m in to_unload:
-            del sys.modules[m]
-
-    with add_path(module_path):
-        return importlib.import_module(module_name)
+        raise ValueError(f"module_path '{model_root}' is not a directory.")
+
+    module_path = Path(model_root, module_name, "__init__.py")
+
+    # We may want to import a matlab-generated model where the extension
+    #  is in a different directory. This is not a regular use case. It's only
+    #  used in the amici tests and can be removed at any time.
+    #  The models (currently) use the default swig-import and require
+    #  modifying sys.path.
+    module_path_matlab = Path(model_root, f"{module_name}.py")
+    if not module_path.is_file() and module_path_matlab.is_file():
+        with set_path(model_root):
+            return _module_from_path(module_name, module_path_matlab)
+
+    module = _module_from_path(module_name, module_path)
+    module._self = module
+    return module
 
 
 class AmiciVersionError(RuntimeError):
diff --git a/python/sdist/amici/__init__.template.py b/python/sdist/amici/__init__.template.py
index f5e49b03dd..efc8df0617 100644
--- a/python/sdist/amici/__init__.template.py
+++ b/python/sdist/amici/__init__.template.py
@@ -1,9 +1,16 @@
 """AMICI-generated module for model TPL_MODELNAME"""
 
+import datetime
+import os
+import sys
 from pathlib import Path
-
+from typing import TYPE_CHECKING
 import amici
 
+
+if TYPE_CHECKING:
+    from amici.jax import JAXModel
+
 # Ensure we are binary-compatible, see #556
 if "TPL_AMICI_VERSION" != amici.__version__:
     raise amici.AmiciVersionError(
@@ -15,7 +22,44 @@
         "version currently installed."
     )
 
-from .TPL_MODELNAME import *  # noqa: F403, F401
-from .TPL_MODELNAME import getModel as get_model  # noqa: F401
+TPL_MODELNAME = amici._module_from_path(
+    "TPL_MODELNAME.TPL_MODELNAME", Path(__file__).parent / "TPL_MODELNAME.py"
+)
+for var in dir(TPL_MODELNAME):
+    if not var.startswith("_"):
+        globals()[var] = getattr(TPL_MODELNAME, var)
+get_model = TPL_MODELNAME.getModel
+
+try:
+    # _self: this module; will be set during import
+    #  via amici.import_model_module
+    TPL_MODELNAME._model_module = _self  # noqa: F821
+except NameError:
+    # when the model package is imported via `import`
+    TPL_MODELNAME._model_module = sys.modules[__name__]
+
+
+def get_jax_model() -> "JAXModel":
+    # If the model directory was meanwhile overwritten, this would load the
+    #  new version, which would not match the previously imported extension.
+    #  This is not allowed, as it would lead to inconsistencies.
+    jax_py_file = Path(__file__).parent / "jax.py"
+    jax_py_file = jax_py_file.resolve()
+    t_imported = TPL_MODELNAME._get_import_time()  # noqa: protected-access
+    t_modified = os.path.getmtime(jax_py_file)
+    if t_imported < t_modified:
+        t_imp_str = datetime.datetime.fromtimestamp(t_imported).isoformat()
+        t_mod_str = datetime.datetime.fromtimestamp(t_modified).isoformat()
+        raise RuntimeError(
+            f"Refusing to import {jax_py_file} which was changed since "
+            f"TPL_MODELNAME was imported. This is to avoid inconsistencies "
+            "between the different model implementations.\n"
+            f"Imported at {t_imp_str}\nModified at {t_mod_str}.\n"
+            "Import the module with a different name or restart the "
+            "Python kernel."
+        )
+    jax = amici._module_from_path("jax", jax_py_file)
+    return jax.JAXModel_TPL_MODELNAME()
+
 
 __version__ = "TPL_PACKAGE_VERSION"
diff --git a/python/sdist/amici/__main__.py b/python/sdist/amici/__main__.py
index bf179cf871..398c975dea 100644
--- a/python/sdist/amici/__main__.py
+++ b/python/sdist/amici/__main__.py
@@ -2,7 +2,13 @@
 
 import sys
 
-from . import __version__, compiledWithOpenMP, has_clibs, hdf5_enabled
+from . import (
+    __version__,
+    compiledWithOpenMP,
+    has_clibs,
+    hdf5_enabled,
+    CpuTimer,
+)
 
 
 def print_info():
@@ -20,6 +26,9 @@ def print_info():
     if hdf5_enabled:
         features.append("HDF5")
 
+    if CpuTimer.uses_thread_clock:
+        features.append("thread_clock")
+
     print(
         f"AMICI ({sys.platform}) version {__version__} ({','.join(features)})"
     )
diff --git a/python/sdist/amici/de_export.py b/python/sdist/amici/de_export.py
index 6b1392a3d1..416dec5694 100644
--- a/python/sdist/amici/de_export.py
+++ b/python/sdist/amici/de_export.py
@@ -21,6 +21,8 @@
     TYPE_CHECKING,
     Literal,
 )
+from itertools import chain
+
 import sympy as sp
 
 from . import (
@@ -54,6 +56,7 @@
     AmiciCxxCodePrinter,
     get_switch_statement,
 )
+from .jaxcodeprinter import AmiciJaxCodePrinter
 from .de_model import DEModel
 from .de_model_components import *
 from .import_utils import (
@@ -143,7 +146,10 @@ class DEExporter:
         If the given model uses special functions, this set contains hints for
         model building.
 
-    :ivar _code_printer:
+    :ivar _code_printer_jax:
+        Code printer to generate JAX code
+
+    :ivar _code_printer_cpp:
         Code printer to generate C++ code
 
     :ivar generate_sensitivity_code:
@@ -212,14 +218,15 @@ def __init__(
         self.set_name(model_name)
         self.set_paths(outdir)
 
-        self._code_printer = AmiciCxxCodePrinter()
+        self._code_printer_cpp = AmiciCxxCodePrinter()
+        self._code_printer_jax = AmiciJaxCodePrinter()
         for fun in CUSTOM_FUNCTIONS:
-            self._code_printer.known_functions[fun["sympy"]] = fun["c++"]
+            self._code_printer_cpp.known_functions[fun["sympy"]] = fun["c++"]
 
         # Signatures and properties of generated model functions (see
         # include/amici/model.h for details)
         self.model: DEModel = de_model
-        self._code_printer.known_functions.update(
+        self._code_printer_cpp.known_functions.update(
             splines.spline_user_functions(
                 self.model._splines, self._get_index("p")
             )
@@ -242,6 +249,7 @@ def generate_model_code(self) -> None:
             sp.Pow, "_eval_derivative", _custom_pow_eval_derivative
         ):
             self._prepare_model_folder()
+            self._generate_jax_code()
             self._generate_c_code()
             self._generate_m_code()
 
@@ -269,6 +277,121 @@ def _prepare_model_folder(self) -> None:
             if os.path.isfile(file_path):
                 os.remove(file_path)
 
+    @log_execution_time("generating jax code", logger)
+    def _generate_jax_code(self) -> None:
+        try:
+            from amici.jax.model import JAXModel
+        except ImportError:
+            logger.warning(
+                "Could not import JAXModel. JAX code will not be generated."
+            )
+            return
+
+        eq_names = (
+            "xdot",
+            "w",
+            "x0",
+            "y",
+            "sigmay",
+            "Jy",
+            "x_solver",
+            "x_rdata",
+            "total_cl",
+        )
+        sym_names = ("x", "tcl", "w", "my", "y", "sigmay", "x_rdata")
+
+        indent = 8
+
+        def jnp_array_str(array) -> str:
+            elems = ", ".join(str(s) for s in array)
+
+            return f"jnp.array([{elems}])"
+
+        # replaces Heaviside variables with corresponding functions
+        subs_heaviside = dict(
+            zip(
+                self.model.sym("h"),
+                [sp.Heaviside(x) for x in self.model.eq("root")],
+                strict=True,
+            )
+        )
+        # replaces observables with a generic my variable
+        subs_observables = dict(
+            zip(
+                self.model.sym("my"),
+                [sp.Symbol("my")] * len(self.model.sym("my")),
+                strict=True,
+            )
+        )
+
+        tpl_data = {
+            # assign named variable using corresponding algebraic formula (function body)
+            **{
+                f"{eq_name.upper()}_EQ": "\n".join(
+                    self._code_printer_jax._get_sym_lines(
+                        (str(strip_pysb(s)) for s in self.model.sym(eq_name)),
+                        self.model.eq(eq_name).subs(
+                            {**subs_heaviside, **subs_observables}
+                        ),
+                        indent,
+                    )
+                )[indent:]  # remove indent for first line
+                for eq_name in eq_names
+            },
+            # create jax array from concatenation of named variables
+            **{
+                f"{eq_name.upper()}_RET": jnp_array_str(
+                    strip_pysb(s) for s in self.model.sym(eq_name)
+                )
+                if self.model.sym(eq_name)
+                else "jnp.array([])"
+                for eq_name in eq_names
+            },
+            # assign named variables from a jax array
+            **{
+                f"{sym_name.upper()}_SYMS": "".join(
+                    str(strip_pysb(s)) + ", " for s in self.model.sym(sym_name)
+                )
+                if self.model.sym(sym_name)
+                else "_"
+                for sym_name in sym_names
+            },
+            # tuple of variable names (ids as they are unique)
+            **{
+                f"{sym_name.upper()}_IDS": "".join(
+                    f'"{strip_pysb(s)}", ' for s in self.model.sym(sym_name)
+                )
+                if self.model.sym(sym_name)
+                else "tuple()"
+                for sym_name in ("p", "k", "y", "x")
+            },
+            **{
+                # in jax model we do not need to distinguish between p (parameters) and
+                # k (fixed parameters) so we use a single variable combining both
+                "PK_SYMS": "".join(
+                    str(strip_pysb(s)) + ", "
+                    for s in chain(self.model.sym("p"), self.model.sym("k"))
+                ),
+                "PK_IDS": "".join(
+                    f'"{strip_pysb(s)}", '
+                    for s in chain(self.model.sym("p"), self.model.sym("k"))
+                ),
+                "MODEL_NAME": self.model_name,
+                # keep track of the API version that the model was generated with so we
+                # can flag conflicts in the future
+                "MODEL_API_VERSION": f"'{JAXModel.MODEL_API_VERSION}'",
+            },
+        }
+        os.makedirs(
+            os.path.join(self.model_path, self.model_name), exist_ok=True
+        )
+
+        apply_template(
+            os.path.join(amiciModulePath, "jax.template.py"),
+            os.path.join(self.model_path, self.model_name, "jax.py"),
+            tpl_data,
+        )
+
     def _generate_c_code(self) -> None:
         """
         Create C++ code files for the model based on
@@ -729,7 +852,7 @@ def _get_function_body(
                                 f"reinitialization_state_idxs.cend(), {index}) != "
                                 "reinitialization_state_idxs.cend())",
                                 f"    {function}[{index}] = "
-                                f"{self._code_printer.doprint(formula)};",
+                                f"{self._code_printer_cpp.doprint(formula)};",
                             ]
                         )
                 cases[ipar] = expressions
@@ -744,12 +867,12 @@ def _get_function_body(
                     f"reinitialization_state_idxs.cend(), {index}) != "
                     "reinitialization_state_idxs.cend())\n        "
                     f"{function}[{index}] = "
-                    f"{self._code_printer.doprint(formula)};"
+                    f"{self._code_printer_cpp.doprint(formula)};"
                 )
 
         elif function in event_functions:
             cases = {
-                ie: self._code_printer._get_sym_lines_array(
+                ie: self._code_printer_cpp._get_sym_lines_array(
                     equations[ie], function, 0
                 )
                 for ie in range(self.model.num_events())
@@ -762,7 +885,7 @@ def _get_function_body(
             for ie, inner_equations in enumerate(equations):
                 inner_lines = []
                 inner_cases = {
-                    ipar: self._code_printer._get_sym_lines_array(
+                    ipar: self._code_printer_cpp._get_sym_lines_array(
                         inner_equations[:, ipar], function, 0
                     )
                     for ipar in range(self.model.num_par())
@@ -777,7 +900,7 @@ def _get_function_body(
             and equations.shape[1] == self.model.num_par()
         ):
             cases = {
-                ipar: self._code_printer._get_sym_lines_array(
+                ipar: self._code_printer_cpp._get_sym_lines_array(
                     equations[:, ipar], function, 0
                 )
                 for ipar in range(self.model.num_par())
@@ -787,7 +910,7 @@ def _get_function_body(
         elif function in multiobs_functions:
             if function == "dJydy":
                 cases = {
-                    iobs: self._code_printer._get_sym_lines_array(
+                    iobs: self._code_printer_cpp._get_sym_lines_array(
                         equations[iobs], function, 0
                     )
                     for iobs in range(self.model.num_obs())
@@ -795,7 +918,7 @@ def _get_function_body(
                 }
             else:
                 cases = {
-                    iobs: self._code_printer._get_sym_lines_array(
+                    iobs: self._code_printer_cpp._get_sym_lines_array(
                         equations[:, iobs], function, 0
                     )
                     for iobs in range(equations.shape[1])
@@ -825,7 +948,7 @@ def _get_function_body(
                     tmp_equations = sp.Matrix(
                         [equations[i] for i in static_idxs]
                     )
-                    tmp_lines = self._code_printer._get_sym_lines_symbols(
+                    tmp_lines = self._code_printer_cpp._get_sym_lines_symbols(
                         tmp_symbols,
                         tmp_equations,
                         function,
@@ -851,7 +974,7 @@ def _get_function_body(
                         [equations[i] for i in dynamic_idxs]
                     )
 
-                    tmp_lines = self._code_printer._get_sym_lines_symbols(
+                    tmp_lines = self._code_printer_cpp._get_sym_lines_symbols(
                         tmp_symbols,
                         tmp_equations,
                         function,
@@ -863,12 +986,12 @@ def _get_function_body(
                         lines.extend(tmp_lines)
 
             else:
-                lines += self._code_printer._get_sym_lines_symbols(
+                lines += self._code_printer_cpp._get_sym_lines_symbols(
                     symbols, equations, function, 4
                 )
 
         else:
-            lines += self._code_printer._get_sym_lines_array(
+            lines += self._code_printer_cpp._get_sym_lines_array(
                 equations, function, 4
             )
 
@@ -1024,10 +1147,10 @@ def _write_model_header_cpp(self) -> None:
             "NK": self.model.num_const(),
             "O2MODE": "amici::SecondOrderMode::none",
             # using code printer ensures proper handling of nan/inf
-            "PARAMETERS": self._code_printer.doprint(self.model.val("p"))[
+            "PARAMETERS": self._code_printer_cpp.doprint(self.model.val("p"))[
                 1:-1
             ],
-            "FIXED_PARAMETERS": self._code_printer.doprint(
+            "FIXED_PARAMETERS": self._code_printer_cpp.doprint(
                 self.model.val("k")
             )[1:-1],
             "PARAMETER_NAMES_INITIALIZER_LIST": self._get_symbol_name_initializer_list(
@@ -1221,7 +1344,7 @@ def _get_symbol_id_initializer_list(self, name: str) -> str:
             Template initializer list of ids
         """
         return "\n".join(
-            f'"{self._code_printer.doprint(symbol)}", // {name}[{idx}]'
+            f'"{self._code_printer_cpp.doprint(symbol)}", // {name}[{idx}]'
             for idx, symbol in enumerate(self.model.sym(name))
         )
 
diff --git a/python/sdist/amici/jax.template.py b/python/sdist/amici/jax.template.py
new file mode 100644
index 0000000000..367ba9e500
--- /dev/null
+++ b/python/sdist/amici/jax.template.py
@@ -0,0 +1,109 @@
+import jax.numpy as jnp
+from interpax import interp1d
+
+from amici.jax.model import JAXModel
+
+
+class JAXModel_TPL_MODEL_NAME(JAXModel):
+    api_version = TPL_MODEL_API_VERSION
+
+    def __init__(self):
+        super().__init__()
+
+    def _xdot(self, t, x, args):
+
+        pk, tcl = args
+
+        TPL_X_SYMS = x
+        TPL_PK_SYMS = pk
+        TPL_TCL_SYMS = tcl
+        TPL_W_SYMS = self._w(t, x, pk, tcl)
+
+        TPL_XDOT_EQ
+
+        return TPL_XDOT_RET
+
+    def _w(self, t, x, pk, tcl):
+
+        TPL_X_SYMS = x
+        TPL_PK_SYMS = pk
+        TPL_TCL_SYMS = tcl
+
+        TPL_W_EQ
+
+        return TPL_W_RET
+
+    def _x0(self, pk):
+
+        TPL_PK_SYMS = pk
+
+        TPL_X0_EQ
+
+        return TPL_X0_RET
+
+    def _x_solver(self, x):
+
+        TPL_X_RDATA_SYMS = x
+
+        TPL_X_SOLVER_EQ
+
+        return TPL_X_SOLVER_RET
+
+    def _x_rdata(self, x, tcl):
+
+        TPL_X_SYMS = x
+        TPL_TCL_SYMS = tcl
+
+        TPL_X_RDATA_EQ
+
+        return TPL_X_RDATA_RET
+
+    def _tcl(self, x, pk):
+
+        TPL_X_RDATA_SYMS = x
+        TPL_PK_SYMS = pk
+
+        TPL_TOTAL_CL_EQ
+
+        return TPL_TOTAL_CL_RET
+
+    def _y(self, t, x, pk, tcl):
+
+        TPL_X_SYMS = x
+        TPL_PK_SYMS = pk
+        TPL_W_SYMS = self._w(t, x, pk, tcl)
+
+        TPL_Y_EQ
+
+        return TPL_Y_RET
+
+    def _sigmay(self, y, pk):
+        TPL_PK_SYMS = pk
+
+        TPL_Y_SYMS = y
+
+        TPL_SIGMAY_EQ
+
+        return TPL_SIGMAY_RET
+
+
+    def _nllh(self, t, x, pk, tcl, my, iy):
+        y = self._y(t, x, pk, tcl)
+        TPL_Y_SYMS = y
+        TPL_SIGMAY_SYMS = self._sigmay(y, pk)
+
+        TPL_JY_EQ
+
+        return TPL_JY_RET.at[iy].get()
+
+    @property
+    def observable_ids(self):
+        return TPL_Y_IDS
+
+    @property
+    def state_ids(self):
+        return TPL_X_IDS
+
+    @property
+    def parameter_ids(self):
+        return TPL_PK_IDS
diff --git a/python/sdist/amici/jax/__init__.py b/python/sdist/amici/jax/__init__.py
new file mode 100644
index 0000000000..e14d231e1e
--- /dev/null
+++ b/python/sdist/amici/jax/__init__.py
@@ -0,0 +1,6 @@
+"""Interface to facilitate AMICI generated models using JAX"""
+
+from amici.jax.petab import JAXProblem, run_simulations
+from amici.jax.model import JAXModel
+
+__all__ = ["JAXModel", "JAXProblem", "run_simulations"]
diff --git a/python/sdist/amici/jax/model.py b/python/sdist/amici/jax/model.py
new file mode 100644
index 0000000000..a7b274027a
--- /dev/null
+++ b/python/sdist/amici/jax/model.py
@@ -0,0 +1,559 @@
+"""Model simulation using JAX."""
+
+# ruff: noqa: F821 F722
+
+from abc import abstractmethod
+
+import diffrax
+import equinox as eqx
+import jax.numpy as jnp
+import jax
+import jaxtyping as jt
+
+
+class JAXModel(eqx.Module):
+    """
+    JAXModel provides an abstract base class for a JAX-based implementation of an AMICI model. The class implements
+    routines for simulation and evaluation of derived quantities, model specific implementations need to be provided by
+    classes inheriting from JAXModel.
+    """
+
+    MODEL_API_VERSION = "0.0.1"
+    api_version: str
+
+    def __init__(self):
+        if self.api_version != self.MODEL_API_VERSION:
+            raise ValueError(
+                "JAXModel API version mismatch, please regenerate the model class."
+            )
+        super().__init__()
+
+    @abstractmethod
+    def _xdot(
+        self,
+        t: jnp.float_,
+        x: jt.Float[jt.Array, "nxs"],
+        args: tuple[jt.Float[jt.Array, "np"], jt.Float[jt.Array, "ncl"]],
+    ) -> jt.Float[jt.Array, "nxs"]:
+        """
+        Right-hand side of the ODE system.
+
+        :param t: time point
+        :param x: state vector
+        :param args: tuple of parameters and total values for conservation laws
+        :return:
+            Temporal derivative of the state vector x at time point t.
+        """
+        ...
+
+    @abstractmethod
+    def _w(
+        self,
+        t: jt.Float[jt.Array, ""],
+        x: jt.Float[jt.Array, "nxs"],
+        pk: jt.Float[jt.Array, "np"],
+        tcl: jt.Float[jt.Array, "ncl"],
+    ) -> jt.Float[jt.Array, "nw"]:
+        """
+        Compute the expressions, i.e. derived quantities that are used in other parts of the model.
+
+        :param t: time point
+        :param x: state vector
+        :param pk: parameters
+        :param tcl: total values for conservation laws
+        :return:
+            Expression values.
+        """
+        ...
+
+    @abstractmethod
+    def _x0(self, pk: jt.Float[jt.Array, "np"]) -> jt.Float[jt.Array, "nx"]:
+        """
+        Compute the initial state vector.
+
+        :param pk: parameters
+        """
+        ...
+
+    @abstractmethod
+    def _x_solver(
+        self, x: jt.Float[jt.Array, "nx"]
+    ) -> jt.Float[jt.Array, "nxs"]:
+        """
+        Transform the full state vector to the reduced state vector for ODE solving.
+
+        :param x:
+            full state vector
+        :return:
+            reduced state vector
+        """
+        ...
+
+    @abstractmethod
+    def _x_rdata(
+        self, x: jt.Float[jt.Array, "nxs"], tcl: jt.Float[jt.Array, "ncl"]
+    ) -> jt.Float[jt.Array, "nx"]:
+        """
+        Compute the full state vector from the reduced state vector and conservation laws.
+
+        :param x:
+            reduced state vector
+        :param tcl:
+            total values for conservation laws
+        :return:
+            full state vector
+        """
+        ...
+
+    @abstractmethod
+    def _tcl(
+        self, x: jt.Float[jt.Array, "nx"], pk: jt.Float[jt.Array, "np"]
+    ) -> jt.Float[jt.Array, "ncl"]:
+        """
+        Compute the total values for conservation laws.
+
+        :param x:
+            state vector
+        :param pk:
+            parameters
+        :return:
+            total values for conservation laws
+        """
+        ...
+
+    @abstractmethod
+    def _y(
+        self,
+        t: jt.Float[jt.Scalar, ""],
+        x: jt.Float[jt.Array, "nxs"],
+        pk: jt.Float[jt.Array, "np"],
+        tcl: jt.Float[jt.Array, "ncl"],
+    ) -> jt.Float[jt.Array, "ny"]:
+        """
+        Compute the observables.
+
+        :param t:
+            time point
+        :param x:
+            state vector
+        :param pk:
+            parameters
+        :param tcl:
+            total values for conservation laws
+        :return:
+            observables
+        """
+        ...
+
+    @abstractmethod
+    def _sigmay(
+        self, y: jt.Float[jt.Array, "ny"], pk: jt.Float[jt.Array, "np"]
+    ) -> jt.Float[jt.Array, "ny"]:
+        """
+        Compute the standard deviations of the observables.
+
+        :param y:
+            observables
+        :param pk:
+            parameters
+        :return:
+            standard deviations of the observables
+        """
+        ...
+
+    @abstractmethod
+    def _nllh(
+        self,
+        t: jt.Float[jt.Scalar, ""],
+        x: jt.Float[jt.Array, "nxs"],
+        pk: jt.Float[jt.Array, "np"],
+        tcl: jt.Float[jt.Array, "ncl"],
+        my: jt.Float[jt.Array, ""],
+        iy: jt.Int[jt.Array, ""],
+    ) -> jt.Float[jt.Scalar, ""]:
+        """
+        Compute the negative log-likelihood of the observable for the specified observable index.
+
+        :param t:
+            time point
+        :param x:
+            state vector
+        :param pk:
+            parameters
+        :param tcl:
+            total values for conservation laws
+        :param my:
+            observed data
+        :param iy:
+            observable index
+        :return:
+            log-likelihood of the observable
+        """
+        ...
+
+    @property
+    @abstractmethod
+    def state_ids(self) -> list[str]:
+        """
+        Get the state ids of the model.
+
+        :return:
+            State ids
+        """
+        ...
+
+    @property
+    @abstractmethod
+    def observable_ids(self) -> list[str]:
+        """
+        Get the observable ids of the model.
+
+        :return:
+            Observable ids
+        """
+        ...
+
+    @property
+    @abstractmethod
+    def parameter_ids(self) -> list[str]:
+        """
+        Get the parameter ids of the model.
+
+        :return:
+            Parameter ids
+        """
+        ...
+
+    def _eq(
+        self,
+        p: jt.Float[jt.Array, "np"],
+        tcl: jt.Float[jt.Array, "ncl"],
+        x0: jt.Float[jt.Array, "nxs"],
+        solver: diffrax.AbstractSolver,
+        controller: diffrax.AbstractStepSizeController,
+        max_steps: jnp.int_,
+    ) -> tuple[jt.Float[jt.Array, "1 nxs"], dict]:
+        """
+        Solve the steady state equation.
+
+        :param p:
+            parameters
+        :param tcl:
+            total values for conservation laws
+        :param x0:
+            initial state vector
+        :param solver:
+            ODE solver
+        :param controller:
+            step size controller
+        :param max_steps:
+            maximum number of steps
+        :return:
+        """
+        sol = diffrax.diffeqsolve(
+            diffrax.ODETerm(self._xdot),
+            solver,
+            args=(p, tcl),
+            t0=0.0,
+            t1=jnp.inf,
+            dt0=None,
+            y0=x0,
+            stepsize_controller=controller,
+            max_steps=max_steps,
+            adjoint=diffrax.DirectAdjoint(),
+            event=diffrax.Event(cond_fn=diffrax.steady_state_event()),
+            throw=False,
+        )
+        return sol.ys[-1, :], sol.stats
+
+    def _solve(
+        self,
+        p: jt.Float[jt.Array, "np"],
+        ts: jt.Float[jt.Array, "nt_dyn"],
+        tcl: jt.Float[jt.Array, "ncl"],
+        x0: jt.Float[jt.Array, "nxs"],
+        solver: diffrax.AbstractSolver,
+        controller: diffrax.AbstractStepSizeController,
+        max_steps: jnp.int_,
+        adjoint: diffrax.AbstractAdjoint,
+    ) -> tuple[jt.Float[jt.Array, "nt nxs"], dict]:
+        """
+        Solve the ODE system.
+
+        :param p:
+            parameters
+        :param ts:
+            time points at which solutions are evaluated
+        :param tcl:
+            total values for conservation laws
+        :param x0:
+            initial state vector
+        :param solver:
+            ODE solver
+        :param controller:
+            step size controller
+        :param max_steps:
+            maximum number of steps
+        :param adjoint:
+            adjoint method
+        :return:
+            solution at time points ts and statistics
+        """
+        sol = diffrax.diffeqsolve(
+            diffrax.ODETerm(self._xdot),
+            solver,
+            args=(p, tcl),
+            t0=0.0,
+            t1=ts[-1],
+            dt0=None,
+            y0=x0,
+            stepsize_controller=controller,
+            max_steps=max_steps,
+            adjoint=adjoint,
+            saveat=diffrax.SaveAt(ts=ts),
+            throw=False,
+        )
+        return sol.ys, sol.stats
+
+    def _x_rdatas(
+        self, x: jt.Float[jt.Array, "nt nxs"], tcl: jt.Float[jt.Array, "ncl"]
+    ) -> jt.Float[jt.Array, "nt nx"]:
+        """
+        Compute the full state vector from the reduced state vector and conservation laws.
+
+        :param x:
+            reduced state vector
+        :param tcl:
+            total values for conservation laws
+        :return:
+            full state vector
+        """
+        return jax.vmap(self._x_rdata, in_axes=(0, None))(x, tcl)
+
+    def _nllhs(
+        self,
+        ts: jt.Float[jt.Array, "nt nx"],
+        xs: jt.Float[jt.Array, "nt nxs"],
+        p: jt.Float[jt.Array, "np"],
+        tcl: jt.Float[jt.Array, "ncl"],
+        mys: jt.Float[jt.Array, "nt"],
+        iys: jt.Int[jt.Array, "nt"],
+    ) -> jt.Float[jt.Array, "nt"]:
+        """
+        Compute the negative log-likelihood for each observable.
+
+        :param ts:
+            time points
+        :param xs:
+            state vectors
+        :param p:
+            parameters
+        :param tcl:
+            total values for conservation laws
+        :param mys:
+            observed data
+        :param iys:
+            observable indices
+        :return:
+            negative log-likelihoods of the observables
+        """
+        return jax.vmap(self._nllh, in_axes=(0, 0, None, None, 0, 0))(
+            ts, xs, p, tcl, mys, iys
+        )
+
+    def _ys(
+        self,
+        ts: jt.Float[jt.Array, "nt"],
+        xs: jt.Float[jt.Array, "nt nxs"],
+        p: jt.Float[jt.Array, "np"],
+        tcl: jt.Float[jt.Array, "ncl"],
+        iys: jt.Float[jt.Array, "nt"],
+    ) -> jt.Int[jt.Array, "nt"]:
+        """
+        Compute the observables.
+
+        :param ts:
+            time points
+        :param xs:
+            state vectors
+        :param p:
+            parameters
+        :param tcl:
+            total values for conservation laws
+        :param iys:
+            observable indices
+        :return:
+            observables
+        """
+        return jax.vmap(
+            lambda t, x, p, tcl, iy: self._y(t, x, p, tcl).at[iy].get(),
+            in_axes=(0, 0, None, None, 0),
+        )(ts, xs, p, tcl, iys)
+
+    def _sigmays(
+        self,
+        ts: jt.Float[jt.Array, "nt"],
+        xs: jt.Float[jt.Array, "nt nxs"],
+        p: jt.Float[jt.Array, "np"],
+        tcl: jt.Float[jt.Array, "ncl"],
+        iys: jt.Int[jt.Array, "nt"],
+    ):
+        """
+        Compute the standard deviations of the observables.
+
+        :param ts:
+            time points
+        :param xs:
+            state vectors
+        :param p:
+            parameters
+        :param tcl:
+            total values for conservation laws
+        :param iys:
+            observable indices
+        :return:
+            standard deviations of the observables
+        """
+        return jax.vmap(
+            lambda t, x, p, tcl, iy: self._sigmay(self._y(t, x, p, tcl), p)
+            .at[iy]
+            .get(),
+            in_axes=(0, 0, None, None, 0),
+        )(ts, xs, p, tcl, iys)
+
+    @eqx.filter_jit
+    def simulate_condition(
+        self,
+        p: jt.Float[jt.Array, "np"],
+        p_preeq: jt.Float[jt.Array, "*np"],
+        ts_preeq: jt.Float[jt.Array, "nt_preeq"],
+        ts_dyn: jt.Float[jt.Array, "nt_dyn"],
+        ts_posteq: jt.Float[jt.Array, "nt_posteq"],
+        my: jt.Float[jt.Array, "nt"],
+        iys: jt.Int[jt.Array, "nt"],
+        solver: diffrax.AbstractSolver,
+        controller: diffrax.AbstractStepSizeController,
+        adjoint: diffrax.AbstractAdjoint,
+        max_steps: int | jnp.int_,
+        ret: str = "llh",
+    ) -> tuple[jt.Float[jt.Array, "nt *nx"] | jnp.float_, dict]:
+        r"""
+        Simulate a condition.
+
+        :param p:
+            parameters for simulation ordered according to ids in :ivar parameter_ids:
+        :param p_preeq:
+            parameters for pre-equilibration ordered according to ids in :ivar parameter_ids:. May be empty to
+            disable pre-equilibration.
+        :param ts_preeq:
+            time points for pre-equilibration. Usually valued 0.0, but needs to be shaped according to
+            the number of observables that are evaluated after pre-equilibration.
+        :param ts_dyn:
+            time points for dynamic simulation. Usually valued > 0.0 and sorted in monotonically increasing order.
+            Duplicate time points are allowed to facilitate the evaluation of multiple observables at specific time
+            points.
+        :param ts_posteq:
+            time points for post-equilibration. Usually valued \Infty, but needs to be shaped according to
+            the number of observables that are evaluated after post-equilibration.
+        :param my:
+            observed data
+        :param iys:
+            indices of the observables according to ordering in :ivar observable_ids:
+        :param solver:
+            ODE solver
+        :param controller:
+            step size controller
+        :param adjoint:
+            adjoint method. Recommended values are `diffrax.DirectAdjoint()` for jax.jacfwd (with vector-valued
+            outputs) and  `diffrax.RecursiveCheckpointAdjoint()` for jax.grad (for scalar-valued outputs).
+        :param max_steps:
+            maximum number of solver steps
+        :param ret:
+            which output to return. Valid values are
+                - `llh`: log-likelihood (default)
+                - `nllhs`: negative log-likelihood at each time point
+                - `x0`: full initial state vector (after pre-equilibration)
+                - `x0_solver`: reduced initial state vector (after pre-equilibration)
+                - `x`: full state vector
+                - `x_solver`: reduced state vector
+                - `y`: observables
+                - `sigmay`: standard deviations of the observables
+                - `tcl`: total values for conservation laws (at final timepoint)
+                - `res`: residuals (observed - simulated)
+        :return:
+            output according to `ret` and statistics
+        """
+        # Pre-equilibration
+        if p_preeq.shape[0] > 0:
+            x0 = self._x0(p_preeq)
+            tcl = self._tcl(x0, p_preeq)
+            current_x = self._x_solver(x0)
+            current_x, stats_preeq = self._eq(
+                p_preeq, tcl, current_x, solver, controller, max_steps
+            )
+            # update tcl with new parameters
+            tcl = self._tcl(self._x_rdata(current_x, tcl), p)
+        else:
+            x0 = self._x0(p)
+            current_x = self._x_solver(x0)
+            stats_preeq = None
+
+            tcl = self._tcl(x0, p)
+        x_preq = jnp.repeat(
+            current_x.reshape(1, -1), ts_preeq.shape[0], axis=0
+        )
+
+        # Dynamic simulation
+        if ts_dyn.shape[0] > 0:
+            x_dyn, stats_dyn = self._solve(
+                p,
+                ts_dyn,
+                tcl,
+                current_x,
+                solver,
+                controller,
+                max_steps,
+                adjoint,
+            )
+            current_x = x_dyn[-1, :]
+        else:
+            x_dyn = jnp.repeat(
+                current_x.reshape(1, -1), ts_dyn.shape[0], axis=0
+            )
+            stats_dyn = None
+
+        # Post-equilibration
+        if ts_posteq.shape[0] > 0:
+            current_x, stats_posteq = self._eq(
+                p, tcl, current_x, solver, controller, max_steps
+            )
+        else:
+            stats_posteq = None
+
+        x_posteq = jnp.repeat(
+            current_x.reshape(1, -1), ts_posteq.shape[0], axis=0
+        )
+
+        ts = jnp.concatenate((ts_preeq, ts_dyn, ts_posteq), axis=0)
+        x = jnp.concatenate((x_preq, x_dyn, x_posteq), axis=0)
+
+        nllhs = self._nllhs(ts, x, p, tcl, my, iys)
+        llh = -jnp.sum(nllhs)
+        return {
+            "llh": llh,
+            "nllhs": nllhs,
+            "x": self._x_rdatas(x, tcl),
+            "x_solver": x,
+            "y": self._ys(ts, x, p, tcl, iys),
+            "sigmay": self._sigmays(ts, x, p, tcl, iys),
+            "x0": self._x_rdata(x[0, :], tcl),
+            "x0_solver": x[0, :],
+            "tcl": tcl,
+            "res": self._ys(ts, x, p, tcl, iys) - my,
+        }[ret], dict(
+            ts=ts,
+            x=x,
+            stats_preeq=stats_preeq,
+            stats_dyn=stats_dyn,
+            stats_posteq=stats_posteq,
+        )
diff --git a/python/sdist/amici/jax/petab.py b/python/sdist/amici/jax/petab.py
new file mode 100644
index 0000000000..6ddfb7c074
--- /dev/null
+++ b/python/sdist/amici/jax/petab.py
@@ -0,0 +1,345 @@
+"""PEtab wrappers for JAX models.""" ""
+
+from numbers import Number
+from collections.abc import Iterable
+
+import diffrax
+import equinox as eqx
+import jaxtyping as jt
+import jax.lax
+import jax.numpy as jnp
+import numpy as np
+import pandas as pd
+import petab.v1 as petab
+
+from amici.petab.parameter_mapping import (
+    ParameterMappingForCondition,
+    create_parameter_mapping,
+)
+from amici.jax.model import JAXModel
+
+
+def jax_unscale(
+    parameter: jnp.float_,
+    scale_str: str,
+) -> jnp.float_:
+    """Unscale parameter according to ``scale_str``.
+
+    Arguments:
+        parameter:
+            Parameter to be unscaled.
+        scale_str:
+            One of ``petab.LIN``, ``petab.LOG``, ``petab.LOG10``.
+
+    Returns:
+        The unscaled parameter.
+    """
+    if scale_str == petab.LIN or not scale_str:
+        return parameter
+    if scale_str == petab.LOG:
+        return jnp.exp(parameter)
+    if scale_str == petab.LOG10:
+        return jnp.power(10, parameter)
+    raise ValueError(f"Invalid parameter scaling: {scale_str}")
+
+
+class JAXProblem(eqx.Module):
+    """
+    PEtab problem wrapper for JAX models.
+
+    :ivar parameters:
+        Values for the model parameters. Do not change dimensions, values may be changed during, e.g. model training.
+    :ivar model:
+        JAXModel instance to use for simulation.
+    :ivar _parameter_mappings:
+        :class:`ParameterMappingForCondition` instances for each simulation condition.
+    :ivar _measurements:
+        Subset measurement dataframes for each simulation condition.
+    :ivar _petab_problem:
+        PEtab problem to simulate.
+    """
+
+    parameters: jnp.ndarray
+    model: JAXModel
+    _parameter_mappings: dict[str, ParameterMappingForCondition]
+    _measurements: dict[
+        tuple[str, ...],
+        tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray],
+    ]
+    _petab_problem: petab.Problem
+
+    def __init__(self, model: JAXModel, petab_problem: petab.Problem):
+        """
+        Initialize a JAXProblem instance with a model and a PEtab problem.
+
+        :param model:
+            JAXModel instance to use for simulation.
+        :param petab_problem:
+            PEtab problem to simulate.
+        """
+        self.model = model
+        scs = petab_problem.get_simulation_conditions_from_measurement_df()
+        self._petab_problem = petab_problem
+        self._parameter_mappings = self._get_parameter_mappings(scs)
+        self._measurements = self._get_measurements(scs)
+        self.parameters = self._get_nominal_parameter_values()
+
+    def _get_parameter_mappings(
+        self, simulation_conditions: pd.DataFrame
+    ) -> dict[str, ParameterMappingForCondition]:
+        """
+        Create parameter mappings for the provided simulation conditions.
+
+        :param simulation_conditions:
+            Simulation conditions to create parameter mappings for. Same format as returned by
+            :meth:`petab.Problem.get_simulation_conditions_from_measurement_df`.
+        :return:
+            Dictionary mapping simulation conditions to parameter mappings.
+        """
+        scs = list(set(simulation_conditions.values.flatten()))
+        mappings = create_parameter_mapping(
+            petab_problem=self._petab_problem,
+            simulation_conditions=[
+                {petab.SIMULATION_CONDITION_ID: sc} for sc in scs
+            ],
+            scaled_parameters=False,
+        )
+        for mapping in mappings:
+            for sim_var, value in mapping.map_sim_var.items():
+                if isinstance(value, Number) and not np.isfinite(value):
+                    mapping.map_sim_var[sim_var] = 1.0
+        return dict(zip(scs, mappings, strict=True))
+
+    def _get_measurements(
+        self, simulation_conditions: pd.DataFrame
+    ) -> dict[
+        tuple[str],
+        tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray],
+    ]:
+        """
+        Get measurements for the model based on the provided simulation conditions.
+
+        :param simulation_conditions:
+            Simulation conditions to create parameter mappings for. Same format as returned by
+            :meth:`petab.Problem.get_simulation_conditions_from_measurement_df`.
+        :return:
+            Dictionary mapping simulation conditions to measurements (tuple of pre-equilibrium, dynamic,
+            post-equilibrium time points; measurements and observable indices).
+        """
+        measurements = dict()
+        for _, simulation_condition in simulation_conditions.iterrows():
+            query = " & ".join(
+                [f"{k} == '{v}'" for k, v in simulation_condition.items()]
+            )
+            m = self._petab_problem.measurement_df.query(query).sort_values(
+                by=petab.TIME
+            )
+
+            ts = m[petab.TIME].values
+            ts_preeq = ts[np.isfinite(ts) & (ts == 0)]
+            ts_dyn = ts[np.isfinite(ts) & (ts > 0)]
+            ts_posteq = ts[np.logical_not(np.isfinite(ts))]
+            my = m[petab.MEASUREMENT].values
+            iys = np.array(
+                [
+                    self.model.observable_ids.index(oid)
+                    for oid in m[petab.OBSERVABLE_ID].values
+                ]
+            )
+
+            measurements[tuple(simulation_condition)] = (
+                ts_preeq,
+                ts_dyn,
+                ts_posteq,
+                my,
+                iys,
+            )
+        return measurements
+
+    def get_all_simulation_conditions(self) -> tuple[tuple[str, ...], ...]:
+        simulation_conditions = (
+            self._petab_problem.get_simulation_conditions_from_measurement_df()
+        )
+        return tuple(tuple(row) for _, row in simulation_conditions.iterrows())
+
+    def _get_nominal_parameter_values(self) -> jt.Float[jt.Array, "np"]:
+        """
+        Get the nominal parameter values for the model based on the nominal values in the PEtab problem.
+
+        :return:
+            jax array with nominal parameter values
+        """
+        return jnp.array(
+            [
+                petab.scale(
+                    self._petab_problem.parameter_df.loc[
+                        pval, petab.NOMINAL_VALUE
+                    ],
+                    self._petab_problem.parameter_df.loc[
+                        pval, petab.PARAMETER_SCALE
+                    ],
+                )
+                for pval in self.parameter_ids
+            ]
+        )
+
+    @property
+    def parameter_ids(self) -> list[str]:
+        """
+        Parameter ids that are estimated in the PEtab problem. Same ordering as values in :attr:`parameters`.
+
+        :return:
+            PEtab parameter ids
+        """
+        return self._petab_problem.parameter_df[
+            self._petab_problem.parameter_df[petab.ESTIMATE] == 1
+        ].index.tolist()
+
+    def get_petab_parameter_by_id(self, name: str) -> jnp.float_:
+        """
+        Get the value of a PEtab parameter by name.
+
+        :param name:
+            PEtab parameter id, as returned by :attr:`parameter_ids`.
+        :return:
+            Value of the parameter
+        """
+        return self.parameters[self.parameter_ids.index(name)]
+
+    def _unscale(
+        self, p: jt.Float[jt.Array, "np"], scales: tuple[str, ...]
+    ) -> jt.Float[jt.Array, "np"]:
+        """
+        Unscaling of parameters.
+
+        :param p:
+            Parameter values
+        :param scales:
+            Parameter scalings
+        :return:
+            Unscaled parameter values
+        """
+        return jnp.array(
+            [jax_unscale(pval, scale) for pval, scale in zip(p, scales)]
+        )
+
+    def load_parameters(
+        self, simulation_condition: str
+    ) -> jt.Float[jt.Array, "np"]:
+        """
+        Load parameters for a simulation condition.
+
+        :param simulation_condition:
+            Simulation condition to load parameters for.
+        :return:
+            Parameters for the simulation condition.
+        """
+        mapping = self._parameter_mappings[simulation_condition]
+        p = jnp.array(
+            [
+                pval
+                if isinstance(pval := mapping.map_sim_var[pname], Number)
+                else self.get_petab_parameter_by_id(pval)
+                for pname in self.model.parameter_ids
+            ]
+        )
+        pscale = tuple(
+            [
+                mapping.scale_map_sim_var[pname]
+                for pname in self.model.parameter_ids
+            ]
+        )
+        return self._unscale(p, pscale)
+
+    def update_parameters(self, p: jt.Float[jt.Array, "np"]) -> "JAXProblem":
+        """
+        Update parameters for the model.
+
+        :param p:
+            New problem instance with updated parameters.
+        """
+        return eqx.tree_at(lambda p: p.parameters, self, p)
+
+    def run_simulation(
+        self,
+        simulation_condition: tuple[str, ...],
+        solver: diffrax.AbstractSolver,
+        controller: diffrax.AbstractStepSizeController,
+        max_steps: jnp.int_,
+    ) -> tuple[jnp.float_, dict]:
+        """
+        Run a simulation for a given simulation condition.
+
+        :param simulation_condition:
+            Tuple of simulation conditions to run the simulation for. can be a single string (simulation only) or a
+            tuple of strings (pre-equilibration followed by simulation).
+        :param solver:
+            ODE solver to use for simulation
+        :param controller:
+            Step size controller to use for simulation
+        :param max_steps:
+            Maximum number of steps to take during simulation
+        :return:
+            Tuple of log-likelihood and simulation statistics
+        """
+        ts_preeq, ts_dyn, ts_posteq, my, iys = self._measurements[
+            simulation_condition
+        ]
+        p = self.load_parameters(simulation_condition[0])
+        p_preeq = (
+            self.load_parameters(simulation_condition[1])
+            if len(simulation_condition) > 1
+            else jnp.array([])
+        )
+        return self.model.simulate_condition(
+            p=p,
+            p_preeq=p_preeq,
+            ts_preeq=jax.lax.stop_gradient(jnp.array(ts_preeq)),
+            ts_dyn=jax.lax.stop_gradient(jnp.array(ts_dyn)),
+            ts_posteq=jax.lax.stop_gradient(jnp.array(ts_posteq)),
+            my=jax.lax.stop_gradient(jnp.array(my)),
+            iys=jax.lax.stop_gradient(jnp.array(iys)),
+            solver=solver,
+            controller=controller,
+            max_steps=max_steps,
+            adjoint=diffrax.RecursiveCheckpointAdjoint(),
+        )
+
+
+def run_simulations(
+    problem: JAXProblem,
+    simulation_conditions: Iterable[tuple] | None = None,
+    solver: diffrax.AbstractSolver = diffrax.Kvaerno5(),
+    controller: diffrax.AbstractStepSizeController = diffrax.PIDController(
+        rtol=1e-8,
+        atol=1e-8,
+        pcoeff=0.4,
+        icoeff=0.3,
+        dcoeff=0.0,
+    ),
+    max_steps: int = 2**10,
+):
+    """
+    Run simulations for a problem.
+
+    :param problem:
+        Problem to run simulations for.
+    :param simulation_conditions:
+        Simulation conditions to run simulations for.
+    :param solver:
+        ODE solver to use for simulation.
+    :param controller:
+        Step size controller to use for simulation.
+    :param max_steps:
+        Maximum number of steps to take during simulation.
+    :return:
+        Overall negative log-likelihood and condition specific results and statistics.
+    """
+    if simulation_conditions is None:
+        simulation_conditions = problem.get_all_simulation_conditions()
+
+    results = {
+        sc: problem.run_simulation(sc, solver, controller, max_steps)
+        for sc in simulation_conditions
+    }
+    return sum(llh for llh, _ in results.values()), results
diff --git a/python/sdist/amici/jaxcodeprinter.py b/python/sdist/amici/jaxcodeprinter.py
new file mode 100644
index 0000000000..ed9181cc09
--- /dev/null
+++ b/python/sdist/amici/jaxcodeprinter.py
@@ -0,0 +1,56 @@
+"""Jax code generation"""
+
+import re
+from collections.abc import Iterable
+from logging import warning
+
+import sympy as sp
+from sympy.printing.numpy import NumPyPrinter
+
+
+class AmiciJaxCodePrinter(NumPyPrinter):
+    """JAX code printer"""
+
+    def doprint(self, expr: sp.Expr, assign_to: str | None = None) -> str:
+        try:
+            code = super().doprint(expr, assign_to)
+            code = re.sub(r"numpy\.", r"jnp.", code)
+
+            return code
+        except TypeError as e:
+            raise ValueError(
+                f'Encountered unsupported function in expression "{expr}"'
+            ) from e
+
+    def _print_AmiciSpline(self, expr: sp.Expr) -> str:
+        warning("Spline interpolation is support in JAX is untested")
+        # FIXME: untested, where are spline nodes coming from anyways?
+        return f'interp1d(time, {self.doprint(expr.args[2:])}, kind="cubic")'
+
+    def _get_sym_lines(
+        self,
+        symbols: sp.Matrix | Iterable[str],
+        equations: sp.Matrix | Iterable[sp.Expr],
+        indent_level: int,
+    ) -> list[str]:
+        """
+        Generate C++ code for assigning symbolic terms in symbols to C++ array
+        `variable`.
+
+        :param equations:
+            vectors of symbolic expressions
+
+        :param symbols:
+            names of the symbols to assign to
+
+        :param indent_level:
+            indentation level (number of leading blanks)
+
+        :return:
+            C++ code as list of lines
+        """
+        indent = " " * indent_level
+        return [
+            f"{indent}{s} = {self.doprint(e)}"
+            for s, e in zip(symbols, equations)
+        ]
diff --git a/python/sdist/amici/petab/conditions.py b/python/sdist/amici/petab/conditions.py
index ab06e8850d..2d72858580 100644
--- a/python/sdist/amici/petab/conditions.py
+++ b/python/sdist/amici/petab/conditions.py
@@ -156,8 +156,9 @@ def _get_par(model_par, value, mapping):
         key: _get_par(key, val, map_sim_fix)
         for key, val in map_sim_fix.items()
     }
+    map_sim_fix_var = map_sim_fix | map_sim_var
     map_sim_var = {
-        key: _get_par(key, val, dict(map_sim_fix, **map_sim_var))
+        key: _get_par(key, val, map_sim_fix_var)
         for key, val in map_sim_var.items()
     }
 
diff --git a/python/sdist/amici/petab/import_helpers.py b/python/sdist/amici/petab/import_helpers.py
index 70af87c3b3..19afe5b237 100644
--- a/python/sdist/amici/petab/import_helpers.py
+++ b/python/sdist/amici/petab/import_helpers.py
@@ -3,7 +3,6 @@
 Functions for PEtab import that are independent of the model format.
 """
 
-import importlib
 import logging
 import os
 import re
@@ -138,8 +137,7 @@ def _can_import_model(model_name: str, model_output_dir: str | Path) -> bool:
     """
     # try to import (in particular checks version)
     try:
-        with amici.add_path(model_output_dir):
-            model_module = importlib.import_module(model_name)
+        model_module = amici.import_model_module(model_name, model_output_dir)
     except ModuleNotFoundError:
         return False
 
diff --git a/python/sdist/amici/petab/parameter_mapping.py b/python/sdist/amici/petab/parameter_mapping.py
index dc88c1064d..cef4c61e06 100644
--- a/python/sdist/amici/petab/parameter_mapping.py
+++ b/python/sdist/amici/petab/parameter_mapping.py
@@ -309,7 +309,7 @@ def create_parameter_mapping(
     petab_problem: petab.Problem,
     simulation_conditions: pd.DataFrame | list[dict],
     scaled_parameters: bool,
-    amici_model: AmiciModel,
+    amici_model: AmiciModel | None = None,
     **parameter_mapping_kwargs,
 ) -> ParameterMapping:
     """Generate AMICI specific parameter mapping.
@@ -399,7 +399,7 @@ def create_parameter_mapping_for_condition(
     parameter_mapping_for_condition: petab.ParMappingDictQuadruple,
     condition: pd.Series | dict,
     petab_problem: petab.Problem,
-    amici_model: AmiciModel,
+    amici_model: AmiciModel | None = None,
 ) -> ParameterMappingForCondition:
     """Generate AMICI specific parameter mapping for condition.
 
@@ -515,27 +515,38 @@ def create_parameter_mapping_for_condition(
     # have different variable parameters. without splitting,
     # merge_preeq_and_sim_pars_condition below may fail.
     # TODO: This can be done already in parameter mapping creation.
-    variable_par_ids = amici_model.getParameterIds()
-    fixed_par_ids = amici_model.getFixedParameterIds()
-
-    condition_map_preeq_var, condition_map_preeq_fix = _subset_dict(
-        condition_map_preeq, variable_par_ids, fixed_par_ids
-    )
+    if amici_model is not None:
+        variable_par_ids = amici_model.getParameterIds()
+        fixed_par_ids = amici_model.getFixedParameterIds()
+        condition_map_preeq_var, condition_map_preeq_fix = _subset_dict(
+            condition_map_preeq, variable_par_ids, fixed_par_ids
+        )
 
-    (
-        condition_scale_map_preeq_var,
-        condition_scale_map_preeq_fix,
-    ) = _subset_dict(
-        condition_scale_map_preeq, variable_par_ids, fixed_par_ids
-    )
+        (
+            condition_scale_map_preeq_var,
+            condition_scale_map_preeq_fix,
+        ) = _subset_dict(
+            condition_scale_map_preeq, variable_par_ids, fixed_par_ids
+        )
 
-    condition_map_sim_var, condition_map_sim_fix = _subset_dict(
-        condition_map_sim, variable_par_ids, fixed_par_ids
-    )
+        condition_map_sim_var, condition_map_sim_fix = _subset_dict(
+            condition_map_sim, variable_par_ids, fixed_par_ids
+        )
 
-    condition_scale_map_sim_var, condition_scale_map_sim_fix = _subset_dict(
-        condition_scale_map_sim, variable_par_ids, fixed_par_ids
-    )
+        condition_scale_map_sim_var, condition_scale_map_sim_fix = (
+            _subset_dict(
+                condition_scale_map_sim, variable_par_ids, fixed_par_ids
+            )
+        )
+    else:
+        condition_map_preeq_var = condition_map_preeq
+        condition_map_preeq_fix = {}
+        condition_scale_map_preeq_var = condition_scale_map_preeq
+        condition_scale_map_preeq_fix = {}
+        condition_map_sim_var = condition_map_sim
+        condition_map_sim_fix = {}
+        condition_scale_map_sim_var = condition_scale_map_sim
+        condition_scale_map_sim_fix = {}
 
     logger.debug(
         "Fixed parameters preequilibration: " f"{condition_map_preeq_fix}"
diff --git a/python/sdist/amici/petab/petab_import.py b/python/sdist/amici/petab/petab_import.py
index 52b08cfd47..42a4d85dc4 100644
--- a/python/sdist/amici/petab/petab_import.py
+++ b/python/sdist/amici/petab/petab_import.py
@@ -37,8 +37,9 @@ def import_petab_problem(
     model_name: str = None,
     compile_: bool = None,
     non_estimated_parameters_as_constants=True,
+    jax=False,
     **kwargs,
-) -> "amici.Model":
+) -> "amici.Model | amici.JAXModel":
     """
     Create an AMICI model for a PEtab problem.
 
@@ -64,6 +65,9 @@ def import_petab_problem(
         model size and simulation times. If sensitivities with respect to those
         parameters are required, this should be set to ``False``.
 
+    :param jax:
+        Whether to load the jax version of the model.
+
     :param kwargs:
         Additional keyword arguments to be passed to
         :meth:`amici.sbml_import.SbmlImporter.sbml2amici` or
@@ -154,6 +158,16 @@ def import_petab_problem(
 
     # import model
     model_module = amici.import_model_module(model_name, model_output_dir)
+
+    if jax:
+        model = model_module.get_jax_model()
+
+        logger.info(
+            f"Successfully loaded jax model {model_name} "
+            f"from {model_output_dir}."
+        )
+        return model
+
     model = model_module.getModel()
     check_model(amici_model=model, petab_problem=petab_problem)
 
diff --git a/python/sdist/amici/pysb_import.py b/python/sdist/amici/pysb_import.py
index 1a21fef1ca..a273759536 100644
--- a/python/sdist/amici/pysb_import.py
+++ b/python/sdist/amici/pysb_import.py
@@ -180,7 +180,7 @@ def pysb2amici(
     # Sympy code optimizations are incompatible with PySB objects, as
     #  `pysb.Observable` comes with its own `.match` which overrides
     #  `sympy.Basic.match()`, breaking `sympy.codegen.rewriting.optimize`.
-    exporter._code_printer._fpoptimizer = None
+    exporter._code_printer_cpp._fpoptimizer = None
     exporter.generate_model_code()
 
     if compile:
diff --git a/python/sdist/pyproject.toml b/python/sdist/pyproject.toml
index f768077172..6441ac3300 100644
--- a/python/sdist/pyproject.toml
+++ b/python/sdist/pyproject.toml
@@ -71,16 +71,26 @@ test = [
     #  unsupported x86_64 / x86_64h
     "antimony!=2.14; platform_system=='Darwin' and platform_machine in 'x86_64h'",
     "scipy",
-    "pooch"
+    "pooch",
+    "beartype",
 ]
-vis =[
+vis = [
     "matplotlib",
     "seaborn",
 ]
-examples =[
+examples = [
     "jupyter",
     "scipy",
 ]
+jax = [
+    "jax>=0.4.34",
+    "jaxlib>=0.4.34",
+    "diffrax>=0.6.0",
+    "jaxtyping>=0.2.34",
+    "equinox>=0.11.8",
+    "optimistix>=0.0.9",
+    "interpax>=0.3.3",
+]
 
 [project.scripts]
 # amici_import_petab.py is kept for backwards compatibility
@@ -121,5 +131,5 @@ line-length = 79
 extend-include = ["*.ipynb"]
 
 [tool.ruff.lint]
-extend-select = ["B028"]
+extend-select = ["B028", "UP"]
 ignore = ["E402", "F403", "F405", "E741"]
diff --git a/python/tests/conftest.py b/python/tests/conftest.py
index d8d882fcfd..3d4856d084 100644
--- a/python/tests/conftest.py
+++ b/python/tests/conftest.py
@@ -7,7 +7,10 @@
 
 import amici
 import pytest
-from amici.testing import TemporaryDirectoryWinSafe
+from amici.testing import TemporaryDirectoryWinSafe as TemporaryDirectory
+from pathlib import Path
+
+EXAMPLES_DIR = Path(__file__).parent / ".." / "examples"
 
 
 @pytest.fixture(scope="session")
@@ -32,7 +35,7 @@ def sbml_example_presimulation_module():
     )
     module_name = "test_model_presimulation"
 
-    with TemporaryDirectoryWinSafe(prefix=module_name) as outdir:
+    with TemporaryDirectory(prefix=module_name) as outdir:
         sbml_importer.sbml2amici(
             model_name=module_name,
             output_dir=outdir,
@@ -71,7 +74,7 @@ def pysb_example_presimulation_module():
 
     model.name = "test_model_presimulation_pysb"
 
-    with TemporaryDirectoryWinSafe(prefix=model.name) as outdir:
+    with TemporaryDirectory(prefix=model.name) as outdir:
         pysb2amici(
             model,
             outdir,
@@ -81,3 +84,18 @@ def pysb_example_presimulation_module():
         )
 
         yield amici.import_model_module(model.name, outdir)
+
+
+@pytest.fixture(scope="session")
+def model_units_module():
+    sbml_file = EXAMPLES_DIR / "example_units" / "model_units.xml"
+    module_name = "test_model_units"
+
+    sbml_importer = amici.SbmlImporter(sbml_file)
+
+    with TemporaryDirectory() as outdir:
+        sbml_importer.sbml2amici(model_name=module_name, output_dir=outdir)
+
+        yield amici.import_model_module(
+            module_name=module_name, module_path=outdir
+        )
diff --git a/python/tests/test_edata.py b/python/tests/test_edata.py
index fab49c160e..0baa7443fb 100644
--- a/python/tests/test_edata.py
+++ b/python/tests/test_edata.py
@@ -2,11 +2,12 @@
 
 import amici
 import numpy as np
+import pytest
 from amici.testing import skip_on_valgrind
-from test_sbml_import import model_units_module  # noqa: F401
 
 
 @skip_on_valgrind
+@pytest.mark.usefixtures("model_units_module")
 def test_edata_sensi_unscaling(model_units_module):  # noqa: F811
     """
     ExpData parameters should be used for unscaling initial state
diff --git a/python/tests/test_events.py b/python/tests/test_events.py
index d16877fd2e..87d6738ffc 100644
--- a/python/tests/test_events.py
+++ b/python/tests/test_events.py
@@ -724,7 +724,7 @@ def test_handling_of_fixed_time_point_event_triggers():
     end
     """
     module_name = "test_events_time_based"
-    with TemporaryDirectory(prefix=module_name, delete=False) as outdir:
+    with TemporaryDirectory(prefix=module_name) as outdir:
         antimony2amici(
             ant_model,
             model_name=module_name,
@@ -765,7 +765,7 @@ def test_multiple_event_assignment_with_compartment():
     """
     # watch out for too long path names on windows ...
     module_name = "tst_mltple_ea_w_cmprtmnt"
-    with TemporaryDirectory(prefix=module_name, delete=False) as outdir:
+    with TemporaryDirectory(prefix=module_name) as outdir:
         antimony2amici(
             ant_model,
             model_name=module_name,
diff --git a/python/tests/test_jax.py b/python/tests/test_jax.py
new file mode 100644
index 0000000000..3254667c50
--- /dev/null
+++ b/python/tests/test_jax.py
@@ -0,0 +1,224 @@
+import pytest
+import amici
+
+pytest.importorskip("jax")
+import amici.jax
+
+import jax.numpy as jnp
+import jax
+import diffrax
+import numpy as np
+from beartype import beartype
+
+from amici.pysb_import import pysb2amici
+from amici.testing import TemporaryDirectoryWinSafe, skip_on_valgrind
+from numpy.testing import assert_allclose
+
+pysb = pytest.importorskip("pysb")
+
+jax.config.update("jax_enable_x64", True)
+
+
+ATOL_SIM = 1e-12
+RTOL_SIM = 1e-12
+
+
+@skip_on_valgrind
+def test_conversion():
+    pysb.SelfExporter.cleanup()  # reset pysb
+    pysb.SelfExporter.do_export = True
+
+    model = pysb.Model("conversion")
+    a = pysb.Monomer("A", sites=["s"], site_states={"s": ["a", "b"]})
+    pysb.Initial(a(s="a"), pysb.Parameter("aa0", 1.2))
+    pysb.Rule("conv", a(s="a") >> a(s="b"), pysb.Parameter("kcat", 0.05))
+    pysb.Observable("ab", a(s="b"))
+
+    with TemporaryDirectoryWinSafe(prefix=model.name) as outdir:
+        pysb2amici(model, outdir, verbose=True, observables=["ab"])
+
+        model_module = amici.import_model_module(
+            module_name=model.name, module_path=outdir
+        )
+
+        ts = tuple(np.linspace(0, 1, 10))
+        p = jnp.stack((1.0, 0.1), axis=-1)
+        k = tuple()
+        _test_model(model_module, ts, p, k)
+
+
+@skip_on_valgrind
+@pytest.mark.filterwarnings(
+    "ignore:Model does not contain any initial conditions"
+)
+def test_dimerization():
+    pysb.SelfExporter.cleanup()  # reset pysb
+    pysb.SelfExporter.do_export = True
+
+    model = pysb.Model("dimerization")
+    a = pysb.Monomer("A", sites=["b"])
+    b = pysb.Monomer("B", sites=["a"])
+
+    pysb.Rule(
+        "turnover_a",
+        a(b=None) | None,
+        pysb.Parameter("kdeg_a", 10),
+        pysb.Parameter("ksyn_a", 0.1),
+    )
+    pysb.Rule(
+        "turnover_b",
+        b(a=None) | None,
+        pysb.Parameter("kdeg_b", 0.1),
+        pysb.Parameter("ksyn_b", 10),
+    )
+    pysb.Rule(
+        "dimer",
+        a(b=None) + b(a=None) | a(b=1) % b(a=1),
+        pysb.Parameter("kon", 1.0),
+        pysb.Parameter("koff", 0.1),
+    )
+
+    pysb.Observable("a_obs", a())
+    pysb.Observable("b_obs", b())
+
+    with TemporaryDirectoryWinSafe(prefix=model.name) as outdir:
+        pysb2amici(
+            model,
+            outdir,
+            verbose=True,
+            observables=["a_obs", "b_obs"],
+            constant_parameters=["ksyn_a", "ksyn_b"],
+        )
+
+        model_module = amici.import_model_module(
+            module_name=model.name, module_path=outdir
+        )
+
+        ts = tuple(np.linspace(0, 1, 10))
+        p = jnp.stack((5, 0.5, 0.5, 0.5), axis=-1)
+        k = (0.5, 5)
+        _test_model(model_module, ts, p, k)
+
+
+def _test_model(model_module, ts, p, k):
+    amici_model = model_module.getModel()
+
+    amici_model.setTimepoints(np.asarray(ts, dtype=np.float64))
+    sol_amici_ref = amici.runAmiciSimulation(
+        amici_model, amici_model.getSolver()
+    )
+
+    jax_model = model_module.get_jax_model()
+
+    amici_model.setParameters(np.asarray(p, dtype=np.float64))
+    amici_model.setFixedParameters(np.asarray(k, dtype=np.float64))
+    edata = amici.ExpData(sol_amici_ref, 1.0, 1.0)
+    edata.parameters = amici_model.getParameters()
+    edata.fixedParameters = amici_model.getFixedParameters()
+    edata.pscale = amici_model.getParameterScale()
+    amici_solver = amici_model.getSolver()
+    amici_solver.setSensitivityMethod(amici.SensitivityMethod.forward)
+    amici_solver.setSensitivityOrder(amici.SensitivityOrder.first)
+    amici_solver.setAbsoluteTolerance(ATOL_SIM)
+    amici_solver.setRelativeTolerance(RTOL_SIM)
+    rs_amici = amici.runAmiciSimulations(amici_model, amici_solver, [edata])
+
+    check_fields_jax(
+        rs_amici, jax_model, edata, ["x", "y", "llh", "res", "x0"]
+    )
+
+    check_fields_jax(
+        rs_amici,
+        jax_model,
+        edata,
+        ["sllh", "sx0", "sx", "sres", "sy"],
+        sensi_order=amici.SensitivityOrder.first,
+    )
+
+
+def check_fields_jax(
+    rs_amici,
+    jax_model,
+    edata,
+    fields,
+    sensi_order=amici.SensitivityOrder.none,
+):
+    r_jax = dict()
+    ts = np.array(edata.getTimepoints())
+    my = np.array(edata.getObservedData()).reshape(len(ts), -1)
+    ts = np.repeat(ts.reshape(-1, 1), my.shape[1], axis=1)
+    iys = np.repeat(np.arange(my.shape[1]).reshape(1, -1), len(ts), axis=0)
+    my = my.flatten()
+    ts = ts.flatten()
+    iys = iys.flatten()
+
+    ts_preeq = ts[ts == 0]
+    ts_dyn = ts[ts > 0]
+    ts_posteq = np.array([])
+    p = jnp.array(list(edata.parameters) + list(edata.fixedParameters))
+    args = (
+        jnp.array([]),  # p_preeq
+        jnp.array(ts_preeq),  # ts_preeq
+        jnp.array(ts_dyn),  # ts_dyn
+        jnp.array(ts_posteq),  # ts_posteq
+        jnp.array(my),  # my
+        jnp.array(iys),  # iys
+        diffrax.Kvaerno5(),  # solver
+        diffrax.PIDController(atol=ATOL_SIM, rtol=RTOL_SIM),  # controller
+        diffrax.RecursiveCheckpointAdjoint(),  # adjoint
+        2**8,  # max_steps
+    )
+    fun = beartype(jax_model.simulate_condition)
+
+    for output in ["llh", "x0", "x", "y", "res"]:
+        oargs = (*args[:-2], diffrax.DirectAdjoint(), 2**8, output)
+        if sensi_order == amici.SensitivityOrder.none:
+            r_jax[output] = fun(p, *oargs)[0]
+        if sensi_order == amici.SensitivityOrder.first:
+            if output == "llh":
+                r_jax[f"s{output}"] = jax.grad(fun, has_aux=True)(p, *args)[0]
+            else:
+                r_jax[f"s{output}"] = jax.jacfwd(fun, has_aux=True)(p, *oargs)[
+                    0
+                ]
+
+    for field in fields:
+        for r_amici, r_jax in zip(rs_amici, [r_jax]):
+            actual = r_jax[field]
+            desired = r_amici[field]
+            if field == "x":
+                actual = actual[iys == 0, :]
+            if field == "y":
+                actual = np.stack(
+                    [actual[iys == iy] for iy in sorted(np.unique(iys))],
+                    axis=1,
+                )
+            elif field == "sllh":
+                actual = actual[: len(edata.parameters)]
+            elif field == "sx":
+                actual = np.permute_dims(
+                    actual[iys == 0, :, : len(edata.parameters)], (0, 2, 1)
+                )
+            elif field == "sy":
+                actual = np.permute_dims(
+                    np.stack(
+                        [
+                            actual[iys == iy, : len(edata.parameters)]
+                            for iy in sorted(np.unique(iys))
+                        ],
+                        axis=1,
+                    ),
+                    (0, 2, 1),
+                )
+            elif field == "sx0":
+                actual = actual[:, : len(edata.parameters)].T
+            elif field == "sres":
+                actual = actual[:, : len(edata.parameters)]
+
+            assert_allclose(
+                actual=actual,
+                desired=desired,
+                atol=1e-5,
+                rtol=1e-5,
+                err_msg=f"field {field} does not match",
+            )
diff --git a/python/tests/test_observable_events.py b/python/tests/test_observable_events.py
index 2887308ff6..db6fc3d452 100644
--- a/python/tests/test_observable_events.py
+++ b/python/tests/test_observable_events.py
@@ -149,8 +149,8 @@ def model_events_def():
 
 
 models = [
-    (model_neuron_def, "model_neuron", ["v0", "I0"]),
-    (model_events_def, "model_events", ["k1", "k2", "k3", "k4"]),
+    (model_neuron_def, "model_neuron_py", ["v0", "I0"]),
+    (model_events_def, "model_events_py", ["k1", "k2", "k3", "k4"]),
 ]
 
 
@@ -197,6 +197,10 @@ def run_test_cases(model):
     solver = model.getSolver()
 
     model_name = model.getName()
+    # we need a different name for the model module to avoid collisions
+    #  with the matlab-pregenerated models, but we need the old name for
+    #  the expected results
+    model_name = model_name.removesuffix("_py")
 
     for case in list(expected_results[model_name].keys()):
         if case.startswith("sensi2"):
@@ -210,7 +214,7 @@ def run_test_cases(model):
         )
 
         edata = None
-        if "data" in expected_results[model.getName()][case].keys():
+        if "data" in expected_results[model_name][case].keys():
             edata = amici.readSimulationExpData(
                 str(expected_results_file),
                 f"/{model_name}/{case}/data",
@@ -226,6 +230,6 @@ def run_test_cases(model):
 
         verify_simulation_results(
             rdata,
-            expected_results[model.getName()][case]["results"],
+            expected_results[model_name][case]["results"],
             **verify_simulation_opts,
         )
diff --git a/python/tests/test_sbml_import.py b/python/tests/test_sbml_import.py
index 4936a3c901..87b18da92d 100644
--- a/python/tests/test_sbml_import.py
+++ b/python/tests/test_sbml_import.py
@@ -2,6 +2,7 @@
 
 import os
 import re
+import sys
 from numbers import Number
 from pathlib import Path
 
@@ -15,13 +16,13 @@
 from amici.testing import skip_on_valgrind
 from numpy.testing import assert_allclose, assert_array_equal
 
-EXAMPLES_DIR = Path(__file__).parent / ".." / "examples"
+from conftest import EXAMPLES_DIR
+
 STEADYSTATE_MODEL_FILE = (
     EXAMPLES_DIR / "example_steadystate" / "model_steadystate_scaled.xml"
 )
 
 
-@pytest.fixture
 def simple_sbml_model():
     """Some testmodel"""
     document = libsbml.SBMLDocument(3, 1)
@@ -44,9 +45,9 @@ def simple_sbml_model():
     return document, model
 
 
-def test_sbml2amici_no_observables(simple_sbml_model):
+def test_sbml2amici_no_observables():
     """Test model generation works for model without observables"""
-    sbml_doc, sbml_model = simple_sbml_model
+    sbml_doc, sbml_model = simple_sbml_model()
     sbml_importer = SbmlImporter(sbml_source=sbml_model, from_file=False)
     model_name = "test_sbml2amici_no_observables"
     with TemporaryDirectory() as tmpdir:
@@ -63,9 +64,9 @@ def test_sbml2amici_no_observables(simple_sbml_model):
 
 
 @skip_on_valgrind
-def test_sbml2amici_nested_observables_fail(simple_sbml_model):
+def test_sbml2amici_nested_observables_fail():
     """Test model generation works for model without observables"""
-    sbml_doc, sbml_model = simple_sbml_model
+    sbml_doc, sbml_model = simple_sbml_model()
     sbml_importer = SbmlImporter(sbml_source=sbml_model, from_file=False)
     model_name = "test_sbml2amici_nested_observables_fail"
     with TemporaryDirectory() as tmpdir:
@@ -83,8 +84,8 @@ def test_sbml2amici_nested_observables_fail(simple_sbml_model):
             )
 
 
-def test_nosensi(simple_sbml_model):
-    sbml_doc, sbml_model = simple_sbml_model
+def test_nosensi():
+    sbml_doc, sbml_model = simple_sbml_model()
     sbml_importer = SbmlImporter(sbml_source=sbml_model, from_file=False)
     model_name = "test_nosensi"
     with TemporaryDirectory() as tmpdir:
@@ -109,9 +110,9 @@ def test_nosensi(simple_sbml_model):
         assert rdata.status == amici.AMICI_ERROR
 
 
-@pytest.fixture
-def observable_dependent_error_model(simple_sbml_model):
-    sbml_doc, sbml_model = simple_sbml_model
+@pytest.fixture(scope="session")
+def observable_dependent_error_model():
+    sbml_doc, sbml_model = simple_sbml_model()
     # add parameter and rate rule
     sbml_model.getSpecies("S1").setInitialConcentration(1.0)
     sbml_model.getParameter("p1").setValue(0.2)
@@ -198,6 +199,7 @@ def test_logging_works(observable_dependent_error_model, caplog):
 @skip_on_valgrind
 def test_model_module_is_set(observable_dependent_error_model):
     model_module = observable_dependent_error_model
+    assert model_module.getModel().module is model_module
     assert isinstance(model_module.getModel().module, amici.ModelModule)
 
 
@@ -229,21 +231,6 @@ def model_steadystate_module():
         )
 
 
-@pytest.fixture(scope="session")
-def model_units_module():
-    sbml_file = EXAMPLES_DIR / "example_units" / "model_units.xml"
-    module_name = "test_model_units"
-
-    sbml_importer = amici.SbmlImporter(sbml_file)
-
-    with TemporaryDirectory() as outdir:
-        sbml_importer.sbml2amici(model_name=module_name, output_dir=outdir)
-
-        yield amici.import_model_module(
-            module_name=module_name, module_path=outdir
-        )
-
-
 def test_presimulation(sbml_example_presimulation_module):
     """Test 'presimulation' test model"""
     model = sbml_example_presimulation_module.getModel()
@@ -521,6 +508,7 @@ def test_likelihoods_error():
 
 
 @skip_on_valgrind
+@pytest.mark.usefixtures("model_units_module")
 def test_units(model_units_module):
     """
     Test whether SBML import works for models using sbml:units annotations.
@@ -694,9 +682,9 @@ def test_code_gen_uses_lhs_symbol_ids():
 
 
 @skip_on_valgrind
-def test_hardcode_parameters(simple_sbml_model):
+def test_hardcode_parameters():
     """Test model generation works for model without observables"""
-    sbml_doc, sbml_model = simple_sbml_model
+    sbml_doc, sbml_model = simple_sbml_model()
     sbml_importer = SbmlImporter(sbml_source=sbml_model, from_file=False)
     r = sbml_model.createRateRule()
     r.setVariable("S1")
@@ -773,3 +761,94 @@ def test_constraints():
                 amici_solver.getAbsoluteTolerance(),
             )
         )
+
+
+@skip_on_valgrind
+def test_import_same_model_name():
+    """Test for error when loading a model with the same extension name as an
+    already loaded model."""
+    from amici.antimony_import import antimony2amici
+    from amici import import_model_module
+
+    # create three versions of a toy model with different parameter values
+    #  to detect which model was loaded
+    ant_model_1 = """
+    model test_same_extension_error
+        species A = 0
+        p = 1
+        A' = p
+    end
+    """
+    ant_model_2 = ant_model_1.replace("1", "2")
+    ant_model_3 = ant_model_1.replace("1", "3")
+
+    module_name = "test_same_extension"
+    with TemporaryDirectory(prefix=module_name) as outdir:
+        outdir_1 = Path(outdir, "model_1")
+        outdir_2 = Path(outdir, "model_2")
+
+        # import the first two models, with the same name,
+        #  but in different location (this is now supported)
+        antimony2amici(
+            ant_model_1,
+            model_name=module_name,
+            output_dir=outdir_1,
+            compute_conservation_laws=False,
+        )
+
+        antimony2amici(
+            ant_model_2,
+            model_name=module_name,
+            output_dir=outdir_2,
+            compute_conservation_laws=False,
+        )
+
+        model_module_1 = import_model_module(
+            module_name=module_name, module_path=outdir_1
+        )
+        assert model_module_1.get_model().getParameters()[0] == 1.0
+
+        # no error if the same model is loaded again without changes on disk
+        model_module_1b = import_model_module(
+            module_name=module_name, module_path=outdir_1
+        )
+        # downside: the modules will compare as different
+        assert (model_module_1 == model_module_1b) is False
+        assert model_module_1.__file__ == model_module_1b.__file__
+        assert model_module_1b.get_model().getParameters()[0] == 1.0
+
+        model_module_2 = import_model_module(
+            module_name=module_name, module_path=outdir_2
+        )
+        assert model_module_1.get_model().getParameters()[0] == 1.0
+        assert model_module_2.get_model().getParameters()[0] == 2.0
+
+        # import the third model, with the same name and location as the second
+        #  model -- this is not supported, because there is some caching at
+        #  the C level we cannot control (or don't know how to)
+
+        # On Windows, this will give "permission denied" when building the
+        #  extension, because we cannot delete a shared library that is in use
+
+        if sys.platform == "win32":
+            return
+
+        antimony2amici(
+            ant_model_3,
+            model_name=module_name,
+            output_dir=outdir_2,
+        )
+
+        with pytest.raises(RuntimeError, match="in the same location"):
+            import_model_module(module_name=module_name, module_path=outdir_2)
+
+        # this should not affect the previously loaded models
+        assert model_module_1.get_model().getParameters()[0] == 1.0
+        assert model_module_2.get_model().getParameters()[0] == 2.0
+
+        # test that we can still import the model classically if we wanted to:
+        with amici.set_path(outdir_1):
+            import test_same_extension as model_module_1c  # noqa: F401
+
+            assert model_module_1c.get_model().getParameters()[0] == 1.0
+            assert model_module_1c.get_model().module is model_module_1c
diff --git a/python/tests/valgrind-python.supp b/python/tests/valgrind-python.supp
index 16c92e3d1f..93fd8614de 100644
--- a/python/tests/valgrind-python.supp
+++ b/python/tests/valgrind-python.supp
@@ -985,3 +985,12 @@
    fun:_PyTuple_Resize
    ...
 }
+
+{
+   Python
+   Memcheck:Cond
+   fun:PyObject_RichCompareBool
+   fun:tuplerichcompare
+   fun:do_richcompare
+   ...
+}
diff --git a/scripts/installAmiciSource.sh b/scripts/installAmiciSource.sh
index c930696380..b5ef9191f5 100755
--- a/scripts/installAmiciSource.sh
+++ b/scripts/installAmiciSource.sh
@@ -36,5 +36,5 @@ python -m pip install --upgrade pip wheel
 python -m pip install --upgrade pip setuptools cmake_build_extension==0.6.0 numpy petab
 python -m pip install git+https://github.com/pysb/pysb@master # for SPM with compartments
 AMICI_BUILD_TEMP="${AMICI_PATH}/python/sdist/build/temp" \
-  python -m pip install --verbose -e "${AMICI_PATH}/python/sdist[petab,test,vis]" --no-build-isolation
+  python -m pip install --verbose -e "${AMICI_PATH}/python/sdist[petab,test,vis,jax]" --no-build-isolation
 deactivate
diff --git a/src/hdf5.cpp b/src/hdf5.cpp
index f9914452eb..c8d4ec4b66 100644
--- a/src/hdf5.cpp
+++ b/src/hdf5.cpp
@@ -10,6 +10,7 @@
 #include <amici/hdf5.h>
 
 #include <amici/edata.h>
+#include <amici/logging.h>
 #include <amici/model.h>
 #include <amici/rdata.h>
 #include <amici/solver.h>
@@ -195,6 +196,47 @@ std::unique_ptr<ExpData> readSimulationExpData(
             ));
     }
 
+    if (locationExists(file, hdf5Root + "/parameters")) {
+        edata->parameters = getDoubleDataset1D(file, hdf5Root + "/parameters");
+    }
+
+    if (locationExists(file, hdf5Root + "/x0")) {
+        edata->x0 = getDoubleDataset1D(file, hdf5Root + "/x0");
+    }
+
+    if (locationExists(file, hdf5Root + "/sx0")) {
+        edata->sx0 = getDoubleDataset1D(file, hdf5Root + "/sx0");
+    }
+
+    if (locationExists(file, hdf5Root + "/pscale")) {
+        auto pscaleInt = getIntDataset1D(file, hdf5Root + "/pscale");
+        edata->pscale.resize(pscaleInt.size());
+        for (int i = 0; (unsigned)i < pscaleInt.size(); ++i)
+            edata->pscale[i] = static_cast<ParameterScaling>(pscaleInt[i]);
+    }
+
+    if (locationExists(file, hdf5Root + "/plist")) {
+        edata->plist = getIntDataset1D(file, hdf5Root + "/plist");
+    }
+
+    if (locationExists(
+            file, hdf5Root + "/reinitialization_state_idxs_presim"
+        )) {
+        edata->reinitialization_state_idxs_presim = getIntDataset1D(
+            file, hdf5Root + "/reinitialization_state_idxs_presim"
+        );
+    }
+
+    if (locationExists(file, hdf5Root + "/reinitialization_state_idxs_sim")) {
+        edata->reinitialization_state_idxs_sim = getIntDataset1D(
+            file, hdf5Root + "/reinitialization_state_idxs_sim"
+        );
+    }
+
+    if (attributeExists(file, hdf5Root, "tstart")) {
+        edata->tstart_ = getDoubleScalarAttribute(file, hdf5Root, "tstart");
+    }
+
     return edata;
 }
 
@@ -262,6 +304,59 @@ void writeSimulationExpData(
         file.getId(), hdf5Location.c_str(),
         "reinitializeFixedParameterInitialStates", &int_attr, 1
     );
+
+    if (!edata.parameters.empty())
+        createAndWriteDouble1DDataset(
+            file, hdf5Location + "/parameters", edata.parameters
+        );
+
+    if (!edata.x0.empty())
+        createAndWriteDouble1DDataset(file, hdf5Location + "/x0", edata.x0);
+    if (!edata.sx0.empty())
+        createAndWriteDouble1DDataset(file, hdf5Location + "/sx0", edata.sx0);
+
+    std::vector<int> int_buffer;
+
+    if (!edata.pscale.empty()) {
+        int_buffer.resize(edata.pscale.size());
+        for (int i = 0; (unsigned)i < edata.pscale.size(); i++)
+            int_buffer[i] = static_cast<int>(edata.pscale[i]);
+        createAndWriteInt1DDataset(file, hdf5Location + "/pscale", int_buffer);
+    }
+
+    if (!edata.plist.empty()) {
+        int_buffer.resize(edata.plist.size());
+        for (int i = 0; (unsigned)i < edata.plist.size(); i++)
+            int_buffer[i] = static_cast<int>(edata.plist[i]);
+        createAndWriteInt1DDataset(file, hdf5Location + "/plist", int_buffer);
+    }
+
+    if (!edata.reinitialization_state_idxs_presim.empty()) {
+        int_buffer.resize(edata.reinitialization_state_idxs_presim.size());
+        for (int i = 0;
+             (unsigned)i < edata.reinitialization_state_idxs_presim.size(); i++)
+            int_buffer[i]
+                = static_cast<int>(edata.reinitialization_state_idxs_presim[i]);
+        createAndWriteInt1DDataset(
+            file, hdf5Location + "/reinitialization_state_idxs_presim",
+            int_buffer
+        );
+    }
+
+    if (!edata.reinitialization_state_idxs_sim.empty()) {
+        int_buffer.resize(edata.reinitialization_state_idxs_sim.size());
+        for (int i = 0;
+             (unsigned)i < edata.reinitialization_state_idxs_sim.size(); i++)
+            int_buffer[i]
+                = static_cast<int>(edata.reinitialization_state_idxs_sim[i]);
+        createAndWriteInt1DDataset(
+            file, hdf5Location + "/reinitialization_state_idxs_sim", int_buffer
+        );
+    }
+
+    H5LTset_attribute_double(
+        file.getId(), hdf5Location.c_str(), "tstart", &edata.tstart_, 1
+    );
 }
 
 void writeReturnData(
@@ -318,6 +413,11 @@ void writeReturnData(
             file, hdf5Location + "/y", rdata.y, rdata.nt, rdata.ny
         );
 
+    if (!rdata.w.empty())
+        createAndWriteDouble2DDataset(
+            file, hdf5Location + "/w", rdata.w, rdata.nt, rdata.nw
+        );
+
     if (!rdata.z.empty())
         createAndWriteDouble2DDataset(
             file, hdf5Location + "/z", rdata.z, rdata.nmaxevent, rdata.nz
@@ -386,6 +486,51 @@ void writeReturnData(
             rdata.nplist, rdata.nz
         );
 
+    // TODO currently unused
+    /*
+    if (!rdata.s2rz.empty())
+        createAndWriteDouble4DDataset(
+            file, hdf5Location + "/s2rz", rdata.s2rz, rdata.nmaxevent,
+            rdata.nztrue, rdata.nplist, rdata.nplist
+        );
+    */
+
+    std::vector<int> int_buffer(1);
+
+    int_buffer[0] = gsl::narrow<int>(rdata.newton_maxsteps);
+    H5LTset_attribute_int(
+        file.getId(), hdf5Location.c_str(), "newton_maxsteps",
+        int_buffer.data(), 1
+    );
+
+    int_buffer[0] = static_cast<int>(rdata.o2mode);
+    H5LTset_attribute_int(
+        file.getId(), hdf5Location.c_str(), "o2mode", int_buffer.data(), 1
+    );
+
+    int_buffer[0] = static_cast<int>(rdata.sensi);
+    H5LTset_attribute_int(
+        file.getId(), hdf5Location.c_str(), "sensi", int_buffer.data(), 1
+    );
+
+    int_buffer[0] = static_cast<int>(rdata.sensi_meth);
+    H5LTset_attribute_int(
+        file.getId(), hdf5Location.c_str(), "sensi_meth", int_buffer.data(), 1
+    );
+
+    int_buffer[0] = static_cast<int>(rdata.rdata_reporting);
+    H5LTset_attribute_int(
+        file.getId(), hdf5Location.c_str(), "rdrm", int_buffer.data(), 1
+    );
+
+    if (!rdata.pscale.empty()) {
+        int_buffer.resize(rdata.pscale.size());
+        for (int i = 0; (unsigned)i < rdata.pscale.size(); i++)
+            int_buffer[i] = static_cast<int>(rdata.pscale[i]);
+        createAndWriteInt1DDataset(file, hdf5Location + "/pscale", int_buffer);
+    }
+    writeLogItemsToHDF5(file, rdata.messages, hdf5Location + "/messages");
+
     writeReturnDataDiagnosis(rdata, file, hdf5Location + "/diagnosis");
 }
 
@@ -540,6 +685,85 @@ void writeReturnDataDiagnosis(
         createAndWriteDouble2DDataset(
             file, hdf5Location + "/J", rdata.J, rdata.nx, rdata.nx
         );
+
+    if (!rdata.x_ss.empty())
+        createAndWriteDouble1DDataset(file, hdf5Location + "/x_ss", rdata.x_ss);
+
+    if (!rdata.sx_ss.empty())
+        createAndWriteDouble2DDataset(
+            file, hdf5Location + "/sx_ss", rdata.sx_ss, rdata.nplist,
+            rdata.nx_rdata
+        );
+}
+
+// work-around for macos segfaults, use struct without std::string
+struct LogItemCStr {
+    int severity;
+    const char* identifier;
+    const char* message;
+};
+
+void writeLogItemsToHDF5(
+    H5::H5File const& file, std::vector<amici::LogItem> const& logItems,
+    std::string const& hdf5Location
+) {
+    if (logItems.empty())
+        return;
+
+    try {
+        hsize_t dims[1] = {logItems.size()};
+        H5::DataSpace dataspace(1, dims);
+
+        // works on Ubuntu, but segfaults on macos:
+        /*
+        // Create a compound datatype for the LogItem struct.
+        H5::CompType logItemType(sizeof(amici::LogItem));
+        logItemType.insertMember(
+            "severity", HOFFSET(amici::LogItem, severity),
+            H5::PredType::NATIVE_INT
+        );
+        auto vlstr_type = H5::StrType(H5::PredType::C_S1, H5T_VARIABLE);
+        logItemType.insertMember(
+            "identifier", HOFFSET(amici::LogItem, identifier), vlstr_type
+        );
+        logItemType.insertMember(
+            "message", HOFFSET(amici::LogItem, message), vlstr_type
+        );
+        H5::DataSet dataset
+            = file.createDataSet(hdf5Location, logItemType, dataspace);
+
+        dataset.write(logItems.data(), logItemType);
+        */
+
+        // ... therefore, as a workaround, we use a struct without std::string
+        H5::CompType logItemType(sizeof(LogItemCStr));
+        logItemType.insertMember(
+            "severity", HOFFSET(LogItemCStr, severity),
+            H5::PredType::NATIVE_INT
+        );
+        auto vlstr_type = H5::StrType(H5::PredType::C_S1, H5T_VARIABLE);
+        logItemType.insertMember(
+            "identifier", HOFFSET(LogItemCStr, identifier), vlstr_type
+        );
+        logItemType.insertMember(
+            "message", HOFFSET(LogItemCStr, message), vlstr_type
+        );
+        H5::DataSet dataset
+            = file.createDataSet(hdf5Location, logItemType, dataspace);
+
+        // Convert std::vector<LogItem> to std::vector<LogItemCStr>
+        std::vector<LogItemCStr> buffer(logItems.size());
+        for (size_t i = 0; i < logItems.size(); ++i) {
+            buffer[i].severity = static_cast<int>(logItems[i].severity);
+            buffer[i].identifier = logItems[i].identifier.c_str();
+            buffer[i].message = logItems[i].message.c_str();
+        }
+
+        // Write the data to the dataset.
+        dataset.write(buffer.data(), logItemType);
+    } catch (H5::Exception& e) {
+        throw AmiException(e.getCDetailMsg());
+    }
 }
 
 void writeReturnData(
@@ -659,6 +883,7 @@ void createAndWriteDouble2DDataset(
     const H5::H5File& file, std::string const& datasetName,
     gsl::span<double const> buffer, hsize_t m, hsize_t n
 ) {
+    Expects(buffer.size() == m * n);
     hsize_t const adims[]{m, n};
     H5::DataSpace dataspace(2, adims);
     auto dataset = file.createDataSet(
@@ -671,6 +896,7 @@ void createAndWriteInt2DDataset(
     H5::H5File const& file, std::string const& datasetName,
     gsl::span<int const> buffer, hsize_t m, hsize_t n
 ) {
+    Expects(buffer.size() == m * n);
     hsize_t const adims[]{m, n};
     H5::DataSpace dataspace(2, adims);
     auto dataset = file.createDataSet(
@@ -683,6 +909,7 @@ void createAndWriteDouble3DDataset(
     H5::H5File const& file, std::string const& datasetName,
     gsl::span<double const> buffer, hsize_t m, hsize_t n, hsize_t o
 ) {
+    Expects(buffer.size() == m * n * o);
     hsize_t const adims[]{m, n, o};
     H5::DataSpace dataspace(3, adims);
     auto dataset = file.createDataSet(
@@ -1388,5 +1615,14 @@ std::vector<double> getDoubleDataset3D(
     return result;
 }
 
+void writeSimulationExpData(
+    ExpData const& edata, std::string const& hdf5Filename,
+    std::string const& hdf5Location
+) {
+    auto file = createOrOpenForWriting(hdf5Filename);
+
+    writeSimulationExpData(edata, file, hdf5Location);
+}
+
 } // namespace hdf5
 } // namespace amici
diff --git a/src/solver.cpp b/src/solver.cpp
index 118af4c8d7..298caaf774 100644
--- a/src/solver.cpp
+++ b/src/solver.cpp
@@ -1035,7 +1035,7 @@ void Solver::setMaxTime(double maxtime) {
 void Solver::startTimer() const { simulation_timer_.reset(); }
 
 bool Solver::timeExceeded(int interval) const {
-    static int eval_counter = 0;
+    thread_local static int eval_counter = 0;
 
     // 0 means infinite time
     if (maxtime_.count() == 0)
diff --git a/src/solver_cvodes.cpp b/src/solver_cvodes.cpp
index 0b60304c55..efe50eb8a9 100644
--- a/src/solver_cvodes.cpp
+++ b/src/solver_cvodes.cpp
@@ -1126,7 +1126,8 @@ static int froot(realtype t, N_Vector x, realtype* root, void* user_data) {
     if (model->ne != model->ne_solver) {
         // temporary buffer to store all root function values, not only the ones
         // tracked by the solver
-        static std::vector<realtype> root_buffer(model->ne, 0.0);
+        thread_local static std::vector<realtype> root_buffer(model->ne, 0.0);
+        root_buffer.resize(model->ne);
         model->froot(t, x, root_buffer);
         std::copy_n(root_buffer.begin(), model->ne_solver, root);
     } else {
diff --git a/swig/modelname.template.i b/swig/modelname.template.i
index 69015dc793..db857348b4 100644
--- a/swig/modelname.template.i
+++ b/swig/modelname.template.i
@@ -1,4 +1,48 @@
-%module TPL_MODELNAME
+%define MODULEIMPORT
+"
+import amici
+import datetime
+import importlib.util
+import os
+import sysconfig
+from pathlib import Path
+
+ext_suffix = sysconfig.get_config_var('EXT_SUFFIX')
+_TPL_MODELNAME = amici._module_from_path(
+    'TPL_MODELNAME._TPL_MODELNAME' if __package__ or '.' in __name__
+    else '_TPL_MODELNAME',
+    Path(__file__).parent / f'_TPL_MODELNAME{ext_suffix}',
+)
+
+def _get_import_time():
+    return _TPL_MODELNAME._get_import_time()
+
+t_imported = _get_import_time()
+t_modified = os.path.getmtime(__file__)
+if t_imported < t_modified:
+    t_imp_str = datetime.datetime.fromtimestamp(t_imported).isoformat()
+    t_mod_str = datetime.datetime.fromtimestamp(t_modified).isoformat()
+    module_path = Path(__file__).resolve()
+    raise RuntimeError(
+        f'Cannot import extension for TPL_MODELNAME from '
+        f'{module_path}, because an extension in the same location '
+        f'has already been imported, but the file was modified on '
+        f'disk. \\nImported at {t_imp_str}\\nModified at {t_mod_str}.\\n'
+        'Import the module with a different name or restart the '
+        'Python kernel.'
+    )
+"
+%enddef
+
+%module(package="TPL_MODELNAME",moduleimport=MODULEIMPORT) TPL_MODELNAME
+
+%pythoncode %{
+# the model-package __init__.py module (will be set during import)
+_model_module = None
+
+
+%}
+
 %import amici.i
 // Add necessary symbols to generated header
 
@@ -9,12 +53,28 @@
 using namespace amici;
 %}
 
+// store the time a module was imported
+%{
+#include <chrono>
+static std::chrono::time_point<std::chrono::system_clock> _module_import_time;
+
+static double _get_import_time() {
+    auto epoch = _module_import_time.time_since_epoch();
+    return std::chrono::duration<double>(epoch).count();
+}
+%}
+
+static double _get_import_time();
+
+%init %{
+    _module_import_time = std::chrono::system_clock::now();
+%}
+
 
 // Make model module accessible from the model
 %feature("pythonappend") amici::generic_model::getModel %{
     if '.' in __name__:
-        import sys
-        val.module = sys.modules['.'.join(__name__.split('.')[:-1])]
+        val.module = _model_module
 %}
 
 
diff --git a/tests/benchmark-models/test_petab_benchmark.py b/tests/benchmark-models/test_petab_benchmark.py
index b055f8961f..7a0afc6832 100644
--- a/tests/benchmark-models/test_petab_benchmark.py
+++ b/tests/benchmark-models/test_petab_benchmark.py
@@ -5,6 +5,7 @@
 for a subset of the benchmark problems.
 """
 
+from functools import partial
 from pathlib import Path
 import fiddy
 import amici
@@ -28,10 +29,12 @@
 from amici.logging import get_logger
 from amici.petab.simulations import (
     LLH,
+    SLLH,
     RDATAS,
     rdatas_to_measurement_df,
     simulate_petab,
 )
+
 from petab.v1.visualize import plot_problem
 
 
@@ -252,6 +255,131 @@ def benchmark_problem(request):
     return problem_id, petab_problem, amici_model
 
 
+@pytest.mark.filterwarnings(
+    "ignore:The following problem parameters were not used *",
+    "ignore: The environment variable *",
+    "ignore:Adjoint sensitivity analysis for models with discontinuous ",
+)
+def test_jax_llh(benchmark_problem):
+    import jax
+    import equinox as eqx
+    import jax.numpy as jnp
+    from amici.jax.petab import run_simulations, JAXProblem
+
+    jax.config.update("jax_enable_x64", True)
+    from beartype import beartype
+
+    problem_id, petab_problem, amici_model = benchmark_problem
+
+    if problem_id in (
+        "Bachmann_MSB2011",
+        "Isensee_JCB2018",
+        "Lucarelli_CellSystems2018",
+        "SalazarCavazos_MBoC2020",
+        "Smith_BMCSystBiol2013",
+    ):
+        # confirmed to work (no gradients) 27/10/2024 but experienced high local runtime (M2 MBA, >30s)
+        pytest.skip("Excluded from JAX check due to excessive runtime")
+
+    amici_solver = amici_model.getSolver()
+    cur_settings = settings[problem_id]
+    amici_solver.setAbsoluteTolerance(1e-8)
+    amici_solver.setRelativeTolerance(1e-8)
+    amici_solver.setMaxSteps(10_000)
+
+    simulate_amici = partial(
+        simulate_petab,
+        petab_problem=petab_problem,
+        amici_model=amici_model,
+        solver=amici_solver,
+        scaled_parameters=True,
+        scaled_gradients=True,
+        log_level=logging.DEBUG,
+    )
+
+    np.random.seed(cur_settings.rng_seed)
+
+    problems_for_gradient_check_jax = list(
+        set(problems_for_gradient_check) - {"Laske_PLOSComputBiol2019"}
+        # Laske has nan values in gradient due to nan values in observables that are not used in the likelihood
+        # but are problematic during backpropagation
+    )
+
+    problem_parameters = None
+    if problem_id in problems_for_gradient_check_jax:
+        point = petab_problem.x_nominal_free_scaled
+        for _ in range(20):
+            amici_solver.setSensitivityMethod(amici.SensitivityMethod.adjoint)
+            amici_solver.setSensitivityOrder(amici.SensitivityOrder.first)
+            amici_model.setSteadyStateSensitivityMode(
+                cur_settings.ss_sensitivity_mode
+            )
+            point_noise = (
+                np.random.randn(len(point)) * cur_settings.noise_level
+            )
+            point += point_noise  # avoid small gradients at nominal value
+
+            problem_parameters = dict(zip(petab_problem.x_free_ids, point))
+
+            r_amici = simulate_amici(
+                problem_parameters=problem_parameters,
+            )
+            if np.isfinite(r_amici[LLH]):
+                break
+        else:
+            raise RuntimeError("Could not compute expected derivative.")
+    else:
+        r_amici = simulate_amici()
+    llh_amici = r_amici[LLH]
+
+    jax_model = import_petab_problem(
+        petab_problem,
+        model_output_dir=benchmark_outdir / problem_id,
+        jax=True,
+    )
+    jax_problem = JAXProblem(jax_model, petab_problem)
+    simulation_conditions = (
+        petab_problem.get_simulation_conditions_from_measurement_df()
+    )
+    simulation_conditions = tuple(
+        tuple(row) for _, row in simulation_conditions.iterrows()
+    )
+    if problem_parameters:
+        jax_problem = eqx.tree_at(
+            lambda x: x.parameters,
+            jax_problem,
+            jnp.array(
+                [problem_parameters[pid] for pid in jax_problem.parameter_ids]
+            ),
+        )
+    if problem_id in problems_for_gradient_check_jax:
+        (llh_jax, _), sllh_jax = eqx.filter_jit(
+            eqx.filter_value_and_grad(run_simulations, has_aux=True)
+        )(jax_problem, simulation_conditions)
+    else:
+        llh_jax, _ = beartype(eqx.filter_jit(run_simulations))(
+            jax_problem, simulation_conditions
+        )
+
+    np.testing.assert_allclose(
+        llh_jax,
+        llh_amici,
+        rtol=1e-3,
+        atol=1e-3,
+        err_msg=f"LLH mismatch for {problem_id}",
+    )
+
+    if problem_id in problems_for_gradient_check_jax:
+        sllh_amici = r_amici[SLLH]
+        np.testing.assert_allclose(
+            sllh_jax.parameters,
+            np.array([sllh_amici[pid] for pid in jax_problem.parameter_ids]),
+            rtol=1e-2,
+            atol=1e-2,
+            err_msg=f"SLLH mismatch for {problem_id}",
+        )
+
+
 @pytest.mark.filterwarnings(
     "ignore:divide by zero encountered in log",
     # https://github.com/AMICI-dev/AMICI/issues/18
diff --git a/version.txt b/version.txt
index 697f087f37..ae6dd4e203 100644
--- a/version.txt
+++ b/version.txt
@@ -1 +1 @@
-0.28.0
+0.29.0