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Transfer learning for deep neural network-based partial differential equations solving

Abstract Deep neural networks (DNNs) have recently shown great potential in solving partial differential equations (PDEs). The success of neural network-based surrogate models is attributed to their ability to learn a rich set of solution-related features. However, learning DNNs usually involves ted...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Xinhai Chen, Chunye Gong, Qian Wan, Liang Deng, Yunbo Wan, Yang Liu, Bo Chen, Jie Liu
Format: Artigo
Sprache:Inglês
Veröffentlicht: SpringerOpen 2021-12-01
Schriftenreihe:Advances in Aerodynamics
Schlagworte:
Deep neural network
Partial differential equation
Surrogate model
Transfer learning
Online-Zugang:https://doi.org/10.1186/s42774-021-00094-7
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