Code accompanying my blog post: So, what is a physics-informed neural network?
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Updated
Mar 22, 2022 - Jupyter Notebook
Code accompanying my blog post: So, what is a physics-informed neural network?
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
Physics-Informed Neural networks for Advanced modeling
IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.
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)
Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing
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
OpenFOAM and Machine Learning Hackathon
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
python library for atomistic machine learning
This repository containts materials for End-to-End AI for Science
PINNs-TF2, Physics-informed Neural Networks (PINNs) implemented in TensorFlow V2.
Code for our RSS'21 paper: "Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control"
Deep learning framework for model reduction of dynamical systems
PINNs-Torch, Physics-informed Neural Networks (PINNs) implemented in PyTorch.
[AAAI-23] Epidemiologically-informed Neural Networks
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