A Pytorch implementation of Extended Physics-Informed Neural Networks (XPINNs)
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Updated
Jul 19, 2024 - Python
A Pytorch implementation of Extended Physics-Informed Neural Networks (XPINNs)
A library for scientific machine learning and physics-informed learning
A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.
FastVPINNs - A tensor-driven acceleration of VPINNs for complex geometries
Generative Pre-Trained Physics-Informed Neural Networks Implementation
Fast lightweight physical informed multi-exposure fusion model.
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.
Scientific Learning project on the monodomain equation
A large-scale benchmark for machine learning methods in fluid dynamics
Codebase for PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs.
DAS: A deep adaptive sampling method for solving high-dimensional partial differential equations
Physics Informed Neural Networks
Machine Learning-based Second-order Analysis of Beam-columns through Physics-Informed Neural Networks
Implementation of a PINN solver for biological differential equations
This repository contains the source code for the research presented in the paper "Exploring hidden flow structures from sparse data through deep-learning-strengthened proper orthogonal decomposition"
A library for scientific machine learning and physics-informed learning
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