Popular GitHub Repositories for Physics Informed Neural Networks

Code accompanying my blog post: So, what is a physics-informed neural network?

PINNs-Torch, Physics-informed Neural Networks (PINNs) implemented in PyTorch.

Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)

A Physics Informed Neural Network made using PyTorch

IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.

A toolkit with data-driven pipelines for physics-informed machine learning.

Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube problems and plane stress linear elasticity boundary value problems

A JAX-based research framework for differentiable and parallelizable acoustic simulations, on CPU, GPUs and TPUs

A pytorch implementaion of physics informed neural networks for two dimensional NS equation

This repository containts materials for End-to-End AI for Science

A Physics-Informed Neural Network to solve 2D steady-state heat equations.

PINNs-TF2, Physics-informed Neural Networks (PINNs) implemented in TensorFlow V2.

A Framework for Remaining Useful Life Prediction Based on Self-Attention and Physics-Informed Neural Networks

Example problems in Physics informed neural network in JAX

OpenFOAM and Machine Learning Hackathon

A curated list of awesome Scientific Machine Learning (SciML) papers, resources and software

[ICLR2025 Spotlight] Official implementation of Conflict-Free Inverse Gradients Method

Here I will try to implement the solution of PDEs using PINN on pytorch for educational purpose

To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to the direction of travel of information in convection-diffusion equations, i.e., method of characteristic; The repository includes a pytorch implementation of PINN and proposed LPINN with periodic boundary conditions