Popular GitHub Repositories for Pde Solver

A differentiable PDE solving framework for machine learning

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

IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.

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

A Library for Advanced Neural PDE Solvers.

Collection of resources about partial differential equations, graph neural networks, deep learning and dynamical system simulation

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

Deep learning library for solving differential equations on top of PyTorch.

Codomain attention neural operator for single to multi-physics PDE adaptation.

JAX-DIPS is a differentiable interfacial PDE solver.

Efficient Differentiable n-d PDE solvers in JAX.

Deep multistep methods to solve BSDEs of first and second order for the approximation of PDE solutions

The lid-driven cavity is a popular problem within the field of computational fluid dynamics (CFD) for validating computational methods. In this repository, we will walk through the process of generating a 2D flow simulation for the Lid Driven Cavity (LDC) flow using Nvidia Modulus framework.

In this study, we propose a novel deep learning model named as the Finite-element-informed neural network (FEI-NN), inspired from finite element method (FEM) for parametric simulation of static problems in structural mechanics.

Implementation in TF 2.0 of Maziar Raissi's Physics Informed Neural Networks (PINNs) repository.

This work proposes a hierarchically normalized physics-informed neural network (hnPINN) to solve PDE problems.

Some code as an introduction to neural networks

Physics-informed neural networks(PINN) use universal function approximators to integrate knowledge of physical laws described by PDEs into the learning process, addressing data scarcity in biological and engineering systems.

Scaling behavior of Physics Informed Neural Networks for solving partial differential equations

Final projects for 401-4656-21L AI in Sciences and Engineering @ ETHz. Includes implementation of Fourier Neural Operator (FNO) with time dependency, data-driven symbolic regression with PDE-Find and foundation model based on FNO for phase-field dynamics