SPiFOL

Learning in infinite dimension with neural operators.

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 library for Koopman Neural Operator with 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

This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs

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