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

Por um escritor misterioso

Last updated 03 março 2025

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 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

Recomendado para você