An exploration of different Physics Informed Machine Learning topics

Scientific Learning project on the monodomain equation

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

Physics-Informed Neural networks for Advanced modeling

The application of a Physics Informed Neural Network on modelling the parameters of a Continuously Stirred Tank Reactor, based on the data generated by a Simulink model.

Repositorio con el material para el taller sobre PINNs en MAPI-3 2024

A general PINN framework for solving ice sheet modeling

The Rheoinformatic lab website

Physics Informed Neural Networks (PINNs) + SPINNs + HyperPINNs with JAX 📓 Check out our various notebooks to get started ⚠️ Mirror repository of jinns (development happens on Gitlab)

This repository contains the code for the "Learning from Integral Losses in Physics Informed Neural Networks" paper (Accepted at ICML 2024, https://arxiv.org/abs/2305.17387).

This repository contains all the machine learning and deep learning model I have implemented using various frameworks like keras, tensorflow, scikit-learn, pytorch, etc.

EIT-EBM

This repository contains the source code and additional resources for the paper "Leveraging Physics-Informed Neural Networks as Solar Wind Forecasting Models". The paper discusses the challenges of solar wind forecasting and the application of Physics-Informed Neural Networks (PiNNs) to improve prediction accuracy and computational efficiency.

python library for atomistic machine learning

Generative Pre-Trained Physics-Informed Neural Networks Implementation

The official respository for noise-aware physics-informed machine learning (nPIML)

Code of the CVPR 2024 paper "Physics-guided Shape-from-Template: Monocular Video Perception through Neural Surrogate Models"

Physics-Informed Neural Networks: Forward/Inverse Modeling of Partial Differential Equations

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

This project contains a collection of deep learning models developed by the AI4Sim team with various partners. This is is structured on the basis of use-cases providing canonical PyTorch Lightning pipelines allowing to train neural network models that are able to surrogate various physical processes.