Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

DeepOnet: Learning nonlinear operators based on the universal approximation theorem of operators.

Physics-Informed Deep Neural Operator Networks – arXiv Vanity

Operator Learning via Physics-Informed DeepONet: Let's Implement It From Scratch, by Shuai Guo

A seamless multiscale operator neural network for inferring bubble dynamics, Journal of Fluid Mechanics

In-context operator learning with data prompts for differential equation problems

High‐Precision and Fast Prediction of Regional Wind Fields in Near Space Using Neural‐Network Approximation of Operators - Chen - 2023 - Geophysical Research Letters - Wiley Online Library

Algorithms, Free Full-Text

2108.06905] A physics-informed variational DeepONet for predicting the crack path in brittle materials - Academus scientific article reader

DeepONet: A deep neural network-based model to approximate linear and nonlinear operators

The Universal Approximation Theorem – deep mind

Brains, Minds + Machines Seminar Series: DeepOnet: Learning nonlinear operators based on the universal approximation theorem of operators

Physics-Informed Neural Operators

Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

Operator Learning via Physics-Informed DeepONet: Let's Implement It From Scratch, by Shuai Guo

Deep-HyROMnet: A Deep Learning-Based Operator Approximation for Hyper-Reduction of Nonlinear Parametrized PDEs