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

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

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

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

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

Generative Pre-Trained Physics-Informed Neural Networks Implementation

Example problems in Physics informed neural network in JAX

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

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

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

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

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

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