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

Learning the solution operator of parametric partial differential equations with physics-informed DeepONets

Simulating progressive intramural damage leading to aortic dissection using DeepONet: an operator–regression neural network

PDF) 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

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

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

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

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

NEW PROGRESS IN INTELLIGENT SOLUTION OF NEURAL OPERATORS AND PHYSICS-INFORMED-BASED METHODS

Learning the solution operator of parametric partial differential equations with physics-informed DeepONets

Lecture Notes in Deep Learning: Known Operator Learning - Part 2 - Pattern Recognition Lab

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

PDF] MIONet: Learning multiple-input operators via tensor product