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.

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)

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.

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

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

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

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

python library for atomistic machine learning

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

Generative Pre-Trained Physics-Informed Neural Networks Implementation

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 cond…

Code for our RSS'21 paper: "Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control"

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

DAS: A deep adaptive sampling method for solving high-dimensional partial differential equations