Popular GitHub Repositories for Coupled Pde

A library for scientific machine learning and physics-informed learning

Learning in infinite dimension with neural operators.

A differentiable PDE solving framework for machine learning

An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations

Welcome to the Physics-based Deep Learning Book v0.3 - the GenAI Edition

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.

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Physics-Informed Neural networks for Advanced modeling

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks

Source code for the Processing Core and Development Environment (PDE)

Project Page of 'Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks'

IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.

A Library for Advanced Neural PDE Solvers.

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

This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs

Codes for TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization.

Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.