physics-informed-neural-networks

NeuroDiffGym/neurodiffeq

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

benmoseley/harmonic-oscillator-pinn

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

rezaakb/pinns-torch

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

mathLab/PINA

Physics-Informed Neural networks for Advanced modeling

benmoseley/FBPINNs

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

nanditadoloi/PINN

Simple PyTorch Implementation of Physics Informed Neural Network (PINN)

idrl-lab/idrlnet

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

IBM/simulai

A toolkit with data-driven pipelines for physics-informed machine learning.

alexpapados/Physics-Informed-Deep-Learning-Solid-and-Fluid-Mechanics

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

openhackathons-org/End-to-End-AI-for-Science

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

ucl-bug/jwave

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

Shengfeng233/PINN-for-NS-equation

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

314arhaam/heat-pinn

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

jbramburger/DataDrivenDynSyst

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

tensordiffeq/TensorDiffEq

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

rezaakb/pinns-tf2

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

XinyuanLiao/AttnPINN-for-RUL-Estimation

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

clegaard/deep_learning_for_dynamical_systems

skoohy/GPT-PINN

Generative Pre-Trained Physics-Informed Neural Networks Implementation

ASEM000/Physics-informed-neural-network-in-JAX

Example problems in Physics informed neural network in JAX

tum-pbs/ConFIG

[ICLR2025 Spotlight] Official implementation of Conflict-Free Inverse Gradients Method

OFDataCommittee/OFMLHackathon

OpenFOAM and Machine Learning Hackathon

MartinuzziFrancesco/awesome-scientific-machine-learning

A curated list of awesome Scientific Machine Learning (SciML) papers, resources and software

rezaakb/pinns-jax

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

rmojgani/LPINNs

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 conditions

vLAR-group/NVFi

NVFi in PyTorch (NeurIPS 2023)

erfanhamdi/pinn-torch

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

thaipduong/SE3HamDL

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

FilippoMB/Physics-Informed-Neural-Networks-tutorial

Hands-on tutorial for implementing Physics Informed Neural Networks in Pytorch

NREL/PINNSTRIPES

Physics-Informed Neural Network SurrogaTe for Rapidly Identifying Parameters in Energy Systems

cmgcds/fastvpinns

FastVPINNs - A tensor-driven acceleration of VPINNs for complex geometries

MartinAstro/GravNN

Repository for Gravity Field Modeling and Recovery using Machine Learning Methods

sgrubas/NES

Neural Eikonal Solver: framework for modeling traveltimes via solving eikonal equation using neural networks

MJfadeaway/DAS

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

killah-t-cell/Plasma.jl

An interface for accelerated simulation of high-dimensional collisionless and electrostatic plasmas.