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Learning function operators with neural networks.
An elegant PyTorch deep reinforcement learning library.
Learning in infinite dimension with neural operators.
Code accompanying my blog post: So, what is a physics-informed neural network?
This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs
A Physics-Informed Neural Network to solve 2D steady-state heat equations.
Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."
[ICLR24] A boundary-embedded neural operator that incorporates complex boundary shape and inhomogeneous boundary values
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
Rheology-informed Machine Learning Projects
A Library for Advanced Neural PDE Solvers.