A Physics-Informed Neural Network to solve 2D steady-state heat equations.
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Updated
Sep 18, 2024 - Jupyter Notebook
A Physics-Informed Neural Network to solve 2D steady-state heat equations.
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
Learning function operators with neural networks.
Codebase for PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs.
Includes codes for the forthcoming paper, "Learning to generate synthetic human mobility data: A physics-regularized Gaussian process approach based on multiple kernel learning"
No need to train, he's a smooth operator
A Physics-informed neural network (PINN) library.
Going through the tutorial on Physics-informed Neural Networks: https://github.com/madagra/basic-pinn
Π-ML: Learn data-driven similarity theories of physical problems
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
All the projects and assignments from HPC-AI specialisation 2022-23
Physics Informed Neural Networks - research in problem solving, architecture improvements, new applications
Applications of PINOs
Supporting code for "reduced order modeling using advection-aware autoencoders"
This repo contains the code for solving Poisson Equation using Physics Informed Neural Networks
Code accompanying my blog post: So, what is a physics-informed neural network?
Physics-based machine learning with dynamic Boltzmann distributions
Deep learning for Engineers - Physics Informed Deep Learning
Deep learning library for solving differential equations and more
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