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

Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.

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.

NodeLab is a simple MATLAB-repository for node-generation and adaptive refinement for testing, and implementing various meshfree methods (including physics-informed neural networks, PINNs and DeepOnet) for solving PDEs in arbitrary domains.

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.

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

This repository implements an Extended Physics-Informed DeepONet (XPIDON) model for simulating autoclave composite curing processes. It features a nonlinear decoder, subdomain-specific input normalization, and an adaptive and automated domain decomposition strategy.

Julia package for generating collocation points inside various domains, with support for variable point density. The primary goal here is to use this for solving PDEs with physics-informed machine learning

Various methods for solving PDEs using artificial intelligence

A short course on Scientific Machine Learning

A comparative analysis of DeepONet and FNO architectures, benchmarking their performance on Function-to-Function (Heat Equation) vs. Parameter-to-Function (Elastic Bar) PDE problems to motivate hybrid operator designs.

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