A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data

Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applications as well as for system identification in science and engineering. Herein, we investigate the performance of two neu...

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Veröffentlicht in:	Computer methods in applied mechanics and engineering Jg. 393; S. 114778
Hauptverfasser:	Lu, Lu, Meng, Xuhui, Cai, Shengze, Mao, Zhiping, Goswami, Somdatta, Zhang, Zhongqiang, Karniadakis, George Em
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.04.2022 Elsevier BV
Schlagworte:	Boundary layers Complexity Computing costs Data management Deep learning DeepONet Domains Fast Fourier transformations Flapping FNO Fourier transforms Function space Instability waves (fluids) Neural networks Nonlinear mappings Operator regression Operators (mathematics) Porous media Scientific machine learning Stability analysis System identification Vorticity Deep learning Operator regression Scientific machine learning DeepONet FNO Nonlinear mappings
ISSN:	0045-7825, 1879-2138
Online-Zugang:	Volltext
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