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

PyTorch Implementation of Physics-informed Neural Networks

Investigating PINNs

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

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

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

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

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

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

Public repository for the proposal “Physics-Informed Machine Learning Simulator for Wildfire Propagation” - MLJC University of Turin - ProjectX2020 Competition (UofT AI)

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

Competitive Physics Informed Networks

Official implementation of Conflict-Free Inverse Gradients Method

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

Yet another PINN implementation

Physics-Informed Neural Networks Trained with Particle Swarm Optimization

Finite Volume PINNs for Hyperbolic Conservation Laws & Compressible Flow

Replication with PyTorch of ''Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations'' by M. Raissi, P. Perdikaris, and G.E. Karniadakis from 2019.