PDEBench: An Extensive Benchmark for Scientific Machine Learning

A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.

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

Physics-Informed Neural networks for Advanced modeling

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

Simple PyTorch Implementation of Physics Informed Neural Network (PINN)

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

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

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

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

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

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

Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems

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

Generative Pre-Trained Physics-Informed Neural Networks Implementation