A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.
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Updated
Jan 19, 2024 - Python
A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.
PDEBench: An Extensive Benchmark for Scientific Machine Learning
Code accompanying my blog post: So, what is a physics-informed neural network?
Physics-Informed Neural networks for Advanced modeling
Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)
Simple PyTorch Implementation of Physics Informed Neural Network (PINN)
IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.
A toolkit with data-driven pipelines for physics-informed machine learning.
A JAX-based research framework for differentiable and parallelizable acoustic simulations, on CPU, GPUs and TPUs
Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube problems and plane stress linear elasticity boundary value problems
Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing
A Physics-Informed Neural Network to solve 2D steady-state heat equation.
A pytorch implementaion of physics informed neural networks for two dimensional NS equation
This repository containts materials for End-to-End AI for Science
Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems
OpenFOAM and Machine Learning Hackathon
Example problems in Physics informed neural network in JAX
A Framework for Remaining Useful Life Prediction Based on Self-Attention and Physics-Informed Neural Networks
python library for atomistic machine learning
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