Popular GitHub Repositories for Pinns

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

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

Physics Informed Machine Learning Tutorials (Pytorch and Jax)

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

Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]

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

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

Introductory workshop on PINNs using the harmonic oscillator

Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing

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

Efficient, Accurate, and Streamlined Training of Physics-Informed Neural Networks

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

Physics Informed Neural Networks (PINNs) + SPINNs + HyperPINNs with JAX ? Check out our various notebooks to get started ?? Mirror repository of jinns (development happens on Gitlab)

NodeLab is a simple MATLAB-repository for node-generation and adaptive refinement for testing, and implementing various meshfree methods (including physics-informed neural networks, PINNs and DeepOnet) for solving PDEs in arbitrary domains.

Simplified implementation of locally adaptive activation functions (LAAF) with slope recovery for deep and physics-informed neural networks (PINNs) in PyTorch.

Gradient-based adaptive sampling algorithms for self-supervising PINNs

The Poisson equation is an integral part of many physical phenomena, yet its computation is often time-consuming. This module presents an efficient method using physics-informed neural networks (PINNs) to rapidly solve arbitrary 2D Poisson problems.

EP-PINNs implementation for 1D and 2D forward and inverse solvers for the Aliev-Panfilov cardiac electrophysiology model. Also includes Matlab finite-differences solver for data generation.

Using NVIDIA modulus for airfoil optimizations at different angles.

resources pour le cours d'introduction à la programmation des GPUs du mastère spécialisé HPC-AI