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Enhanced DeepONet for Modeling Partial Differential Operators
  Considering Multiple Input Functions
v1v2 (latest)

Enhanced DeepONet for Modeling Partial Differential Operators Considering Multiple Input Functions

17 February 2022
Lesley Tan
Liang Chen
ArXiv (abs)PDFHTML

Papers citing "Enhanced DeepONet for Modeling Partial Differential Operators Considering Multiple Input Functions"

3 / 3 papers shown
Title
Generating synthetic data for neural operators
Generating synthetic data for neural operators
Erisa Hasani
Rachel A. Ward
AI4CE
128
8
0
04 Jan 2024
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
Lu Lu
Pengzhan Jin
George Karniadakis
248
2,131
0
08 Oct 2019
DeepXDE: A deep learning library for solving differential equations
DeepXDE: A deep learning library for solving differential equations
Lu Lu
Xuhui Meng
Zhiping Mao
George Karniadakis
PINNAI4CE
97
1,533
0
10 Jul 2019
1