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

Generative Pre-Trained Physics-Informed Neural Networks Implementation

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

Example problems in Physics informed neural network in JAX

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

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…

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

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

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