Popular GitHub Repositories for Pdes

Graph Neural PDEs

PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks

Automatic Differentiation Library for Computational and Mathematical Engineering

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

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

Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.

Generative Pre-Trained Physics-Informed Neural Networks Implementation

A Julia package for Deep Backwards Stochastic Differential Equation (Deep BSDE) and Feynman-Kac methods to solve high-dimensional PDEs without the curse of dimensionality

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

Dimension reduced surrogate construction for parametric PDE maps

GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.

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.

Official repo for separable operator networks -- extreme-scale operator learning for parametric PDEs.

Implement PINN with high level APIs of TF2.0, including a solution of coupled PDEs with PINN

The unsupervised learning problem trains a diffeomorphic spatio-temporal grid, that registers the output sequence of the PDEs onto a non-uniform parameter/time-varying grid, such that the Kolmogorov n-width of the mapped data on the learned grid is minimized.

Physics-informed neural networks (PINNs)

Solving various PDEs by employing Siren, with JAX implementation.

A pytorch framework for solving PDEs via Physics Informed Neural Networks (PINNs)!

Solution of 1D and 2D PDEs using Physics Informed Neural Networks (PINNs)

A Physics-Informed Neural Network (PINN) framework for solving partial differential equations (PDEs) with FastAPI integration. This project implements PINNs for various physical systems including simple harmonic motion, heat transfer, wave propagation, and fluid dynamics. The framework provides a modular architecture for training.