Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
Por um escritor misterioso
Last updated 28 maio 2024
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
Learning Operators with Mesh-Informed Neural Networks
Learning the solution operator of parametric partial differential equations with physics-informed DeepONets
Frontiers Fourier filter-based physics- information convolutional recurrent network for 2D incompressible flow
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
Physics-Informed Neural Operators
VB-DeepONet: A Bayesian operator learning framework for uncertainty quantification - ScienceDirect
Deep-HyROMnet: A Deep Learning-Based Operator Approximation for Hyper-Reduction of Nonlinear Parametrized PDEs
Algorithms, Free Full-Text
PDF) DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
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Mathematics, Free Full-Text
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
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