Reliable extrapolation of deep neural operators informed by physics or sparse observations

Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks. As promising surrogate solvers of partial differential equations (PDEs) for real-time prediction, deep neural operators such as deep operator networks (DeepONets) provide a new s...

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Veröffentlicht in:	Computer methods in applied mechanics and engineering 2023-05, Vol.412
Hauptverfasser:	Zhu, Min, Zhang, Handi, Jiao, Anran, Karniadakis, George Em, Lu, Lu
Format:	Artikel
Sprache:	eng
Schlagworte:	DeepONet ENGINEERING Extrapolation complexity Fine-tuning Multifidelity learning Neural operators Out-of-distribution inference
Online-Zugang:	Volltext
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