A library for scientific machine learning and physics-informed learning

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

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

[NeurIPS 2024] Codebase for PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs.

Physics-informed neural network for solving fluid dynamics problems

A large-scale benchmark for machine learning methods in fluid dynamics

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

Neural network based solvers for partial differential equations and inverse problems :milky_way:. Implementation of physics-informed neural networks in pytorch.

PINN (Physics-Informed Neural Networks) on Navier-Stokes Equations

Generative Pre-Trained Physics-Informed Neural Networks Implementation

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

Example problems in Physics informed neural network in JAX

A carefully curated collection of high-quality libraries, projects, tutorials, research papers, and other essential resources focused on Physics-Informed Machine Learning (PIML) and Physics-Informed Neural Networks (PINNs).

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

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

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

Using PINN based MPC for motion planning for SDC and LSTM for pedestrain's trajectory prediction as dynamic obstacles

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

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

LSTM-PINN and PINN for population forecasting

A remix of Arch Linux ARM for Raspberry Pi 3 B+ built for HackRF and RTL-SDR

A repo to learn and curate PINN

The Poisson equation is an integral part of many physical phenomena, yet its computation is often time-consuming. This module presents an efficient method using physics-informed neural networks (PINNs) to rapidly solve arbitrary 2D Poisson problems.

The first open-source repository dedicated to PINN research via Vibe Coding. Complete JAX-GPU implementations of PINN algorithms with Chinese tutorials. 首个采用 Vibe Coding 进行 PINN 研究的开源仓库。

QCPINN: Quantum-Classical Physics-Informed Neural Networks

This repository contains the machine learning projects completed for the class "Deep Learning in Scientific Computing" taught at ETH jointly by Siddhartha Mishra and Benjamin Moseley in Spring 2024. The description of the tasks can be found in the PDFs.

Implementation of Dabrowski et. al., "Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires"

resources pour le cours d'introduction à la programmation des GPUs du mastère spécialisé HPC-AI