Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
Por um escritor misterioso
Last updated 24 janeiro 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 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
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