pinn

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.

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

Physics-informed neural network for solving fluid dynamics problems

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

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

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

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

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).

Generative Pre-Trained Physics-Informed Neural Networks Implementation

Instructed Glacier Model (IGM)

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

Example problems in Physics informed neural network in JAX

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 cond…