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'.

Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."

No need to train, he's a smooth operator

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".

Benchmarking Surrogates for coupled ODE systems.

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

DeepONets for interpreting x-ray spectra by converting XANES to EXAFS

Implementação de metodologias baseadas em redes neurais para a solução de equações diferenciais parciais.