Multi-scale physics-informed neural networks for solving high Reynolds number boundary layer flows based on matched asymptotic expansions

Multi-scale physics-informed neural networks for solving high Reynolds number boundary layer flows based on matched asymptotic expansions

Multi-scale system remains a classical scientific problem in fluid dynamics, biology, etc. In the present study, a scheme of multi-scale Physics-informed neural networks is proposed to solve the boundary layer flow at high Reynolds numbers without any data. The flow is divided into several regions w...

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Podrobná bibliografie
Hlavní autoři: Jianlin Huang, Rundi Qiu, Jingzhu Wang, Yiwei Wang
Médium: Článek
Jazyk:English
Vydáno: Elsevier 2024-03-01
Edice:Theoretical and Applied Mechanics Letters
Témata:
Physics-informed neural networks (PINNs)
Multi-scale
Fluid dynamics
Boundary layer
On-line přístup:http://www.sciencedirect.com/science/article/pii/S2095034924000072

Internet

http://www.sciencedirect.com/science/article/pii/S2095034924000072

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