Reconstruction of 3D flow field around a building model in wind tunnel: a novel physics-informed neural network framework adopting dynamic prioritization self-adaptive loss balance strategy

Reconstruction of 3D flow field around a building model in wind tunnel: a novel physics-informed neural network framework adopting dynamic prioritization self-adaptive loss balance strategy

Physics-informed neural networks (PINNs) have recently emerged and attracted extensive attention as an alternative approach to computational fluid dynamics (CFD) methods, which can provide competitive solutions to a variety of forward and inverse fluid problems. In this study, we reconstruct a 3D wi...

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Bibliographic Details
Main Authors: En-Ze Rui, Zheng-Wei Chen, Yi-Qing Ni, Lei Yuan, Guang-Zhi Zeng
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:Engineering Applications of Computational Fluid Mechanics
Subjects:
Buildings
Flow fields
Reconstruction
Physics-informed neural network (PINN)
Wind tunnel test
Online Access:https://www.tandfonline.com/doi/10.1080/19942060.2023.2238849

Internet

https://www.tandfonline.com/doi/10.1080/19942060.2023.2238849

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