A library for scientific machine learning and physics-informed learning
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Updated
Aug 16, 2024 - Python
A library for scientific machine learning and physics-informed learning
Physics Informed Machine Learning Tutorials (Pytorch and Jax)
DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.
No need to train, he's a smooth operator
Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."
Code for training and inferring acoustic wave propagation in 3D
Official repo for separable operator networks -- extreme-scale operator learning for parametric PDEs.
Nonlinear model reduction for operator learning
Source code of "On the influence of over-parameterization in manifold based surrogates and deep neural operators".
We implement a Multifidelity-DeepONet that leverages both high-fidelity CFD simulations and real-time, low-fidelity sensor data. We also proved that Multifidelity-DeepONet has better performance compare to all the others baseline methods in our experiments.
RenONet: Multiscale operator learning for complex social systems
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