There are 2 repositories under pinn topic.
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 network for solving fluid dynamics problems
Neural network based solvers for partial differential equations and inverse problems :milky_way:. Implementation of physics-informed neural networks in pytorch.
A large-scale benchmark for machine learning methods in fluid dynamics
Deep learning library for solving differential equations on top of PyTorch.
Example problems in Physics informed neural network in JAX
This repository containts materials for End-to-End AI for Science
PINN (Physics-Informed Neural Networks) on Navier-Stokes Equations
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
DAS: A deep adaptive sampling method for solving high-dimensional partial differential equations
A remix of Arch Linux ARM for Raspberry Pi 3 B+ built for HackRF and RTL-SDR
Here I will try to implement the solution of PDEs using PINN on pytorch for educational purpose
PINN-FWI: performing physics-informed neural network for FWI
resources pour le cours d'introduction à la programmation des GPUs du mastère spécialisé HPC-AI
A curated list of awesome Scientific Machine Learning (SciML) papers, resources and software
Using PINN based MPC for motion planning for SDC and LSTM for pedestrain's trajectory prediction as dynamic obstacles
PiNN2 is a easy-to-use framework for device compact modeling using physics-inspired neural networks
Pytorch implementation of Physics Informed Neural Networks and improvements
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.
Implementation of a Physics Informed Neural Network (PINN) written in Tensorflow v2, which is capable of solving Partial Differential Equations.
A standalone project to test libtorch C++ APIs on solving the 2D heat equation with PINN.
A Framework for building custom Physics Informed Neural Networks (PINNs).
This project is divided in a two parts. In first study, Lame parameters are identified using tanh activation function. After that, six activation functions are analysed on the basis of minimum loss, training time and convergence order for different error norms.
This repo contains the code for solving Poisson Equation using Physics Informed Neural Networks
Using Physics Informed Neural Networks to solve the Burger's Equation
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.