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

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

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

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

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

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

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

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

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