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

DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators

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

PDF) 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

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

A DeepONet multi-fidelity approach for residual learning in reduced order modeling, Advanced Modeling and Simulation in Engineering Sciences

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

PDF] Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks

AI与PDE（三）：大概是最好懂的DeepONet模型解析- 知乎

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

DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators – arXiv Vanity

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