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

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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
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.
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
NEW PROGRESS IN INTELLIGENT SOLUTION OF NEURAL OPERATORS AND PHYSICS-INFORMED-BASED METHODS
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
In-context operator learning with data prompts for differential equation problems
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
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
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
Enhanced DeepONet for Modeling Partial Differential Operators Considering Multiple Input Functions
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
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
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
A seamless multiscale operator neural network for inferring bubble dynamics, Journal of Fluid Mechanics
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
PDF) 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
Physics-Informed Deep Neural Network for Backward-in-Time Prediction: Application to Rayleigh–Bénard Convection in: Artificial Intelligence for the Earth Systems Volume 2 Issue 1 (2023)
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
Why do we need physics-informed machine learning (PIML)?, by Shuai Zhao
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