Update README.md (#17)

README.md

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 # ADEPT
 ADEPT is an **A**utomatic **D**ifferentation **E**nabled **P**lasma **T**ransport code.
 
-It solves the equations of motion for a plasma. 
-
-Currently, it is tested to reproduce
-
-1. Two fluid - Poisson system in 1D
-2. Vlasov-Poisson system in 2D
-
-### What is novel about it?
-- Automatic Differentiation (AD) Enabled (bc of JAX, Diffrax)
-- GPU-capable (bc of JAX, XLA)
-- Experiment manager enabled (bc of mlflow)
-- Pythonic
-
-### What does AD do for us?
-AD enables the calculation of derivatives of entire simulations, pieces of it, or anything in between. This can be used for 
-- sensitivity analyses
-- parameter estimation
-- parameter optimization
-- model training
-
-A couple of implemented examples are
-
-#### Find the resonant frequency given a density and temperature
-This is provided as a test in `tests/test_resonance_search.py`. Also see ref. [1]  
-
-#### Fit a parameteric model for unresolved / unsolved microphysics
-The gist is that there is a discrepancy in the observable between a "first-principles" and "approximate" simulation.
-You would like for that discrepancy to decrease. To do so, you add a neural network to your "approximate" solver in a smart fashion.
-Then, you calibrate the results of your "approximate" simulation against the "ground-truth" from the "first-principles" simulation.
-After doing that enough times, over a diverse enough set of physical parameters, the neural network is able to help your "approximate" solver
-recover the "correct" answer.
-
-See ref. [2] for details and an application
-
-### What does an experiment manager do for us?
-An experiment manager, namely `mlflow` here, simplifies management of simulation configurations and artifacts.
-We run `mlflow` on the cloud so we have a central, web-accessible store for all simulation objects. This saves all the 
-data and simulation management effort and just let `mlflow` manage everything. To see for yourself,
-just run a simulation and type `mlflow ui` into your terminal and see what happens :)
-
-## Current (Tested) Implementation:
-1D Electrostatic Plasma - 3 Moments - $$n(t, x), u(t, x), p(t, x)$$
-
-- Gradients are calculated spectrally using FFTs
-- 4th order explicit time integrator (using Diffrax)
-
-Depending on the flags in the config, it can support
-- Bohm-Gross oscillation frequency (Fluid dispersion relation)
-- Landau damping (through a momentum damping term and a tabulated damping coefficient)
-- Kinetic oscillation frequency (Kinetic dispersion relation by modifying adiabatic index)
-
 ## Installation
 ### Conda
 1. Install `conda` (we recommend `mamba`)
@@ -63,28 +12,12 @@ Depending on the flags in the config, it can support
 2. `source venv/bin/activate`
 3. `pip3 install -r requirements.txt`
 
-## Tests
-The package is tested against 
-- `test_resonance.py` - recover the Bohm-Gross dispersion relation and kinetic dispersion relation just using the forward pass
-- `test_resonance_search.py` - recover the Bohm-Gross dispersion relation and kinetic dispersion relation using the backward pass
-- `test_landau_damping.py` - recover the Landau damping rate according to the kinetic dispersion relation using a phenomenological term
-- `test_against_vlasov.py` - recover a driven warm plasma wave simulation that is nearly identical to a Vlasov-Boltzmann simulation
-
-### Run the tests
-1. `pip install pytest`
-2. `pytest` or `pytest tests/<test_filename>`
-
-## Run custom simulations
-Take one of the `config`s in the `/configs` directory and modify it as you wish. Then use `run.py` to run the simulation
-You will find the results using the mlflow ui. You can find the binary run information will be stored using mlflow as well.
+## Docs
+https://adept.readthedocs.io/en/latest/
 
 ## Contributing guide
-The contributing guide is in development but for now, just make an issue / pull request and we can go from there :)
+The contributing guide is in development but for now, just make an issue / pull request and we can go from there :) 
 
-References:
-[1] A. S. Joglekar and A. G. R. Thomas, “Machine learning of hidden variables in multiscale fluid simulation,” Mach. Learn.: Sci. Technol., vol. 4, no. 3, p. 035049, Sep. 2023, doi: 10.1088/2632-2153/acf81a.
-[2] A. S. Joglekar and A. G. R. Thomas, “Unsupervised discovery of nonlinear plasma physics using differentiable kinetic simulations,” J. Plasma Phys., vol. 88, no. 6, p. 905880608, Dec. 2022, doi: 10.1017/S0022377822000939.
-
+## Citation
 
-Citation:
-[1] A. S. Joglekar and A. G. R. Thomas, “Machine learning of hidden variables in multiscale fluid simulation,” Mach. Learn.: Sci. Technol., vol. 4, no. 3, p. 035049, Sep. 2023, doi: 10.1088/2632-2153/acf81a.
+[1] A. S. Joglekar and A. G. R. Thomas, “Machine learning of hidden variables in multiscale fluid simulation,” Mach. Learn.: Sci. Technol., vol. 4, no. 3, p. 035049, Sep. 2023, doi: 10.1088/2632-2153/acf81a.