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-Reference-Cited by-同舟云学术

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

Published:2023-07-25 Issue:1 Volume:17 Page:
ISSN:1994-2060
Container-title:Engineering Applications of Computational Fluid Mechanics
language:en
Short-container-title:Engineering Applications of Computational Fluid Mechanics

Author:

Rui En-Ze¹²,Chen Zheng-Wei¹²,Ni Yi-Qing¹²,Yuan Lei¹²,Zeng Guang-Zhi¹²

Affiliation:

1. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, People’s Republic of China

2. National Rail Transit Electrification and Automation Engineering Technology Research Center (Hong Kong Branch), Hong Kong, People’s Republic of China

Funder

Hong Kong Polytechnic University

Innovation and Technology Commission (ITC) of the Hong Kong Special Administrative Region (SAR), China

Research Grants Council (RGC) of the Hong Kong Special Administrative Region (SAR), China

Key Laboratory of Traffic Safety on Track of Ministry of Education, Central South University

Publisher

Informa UK Limited

Subject

Modeling and Simulation,General Computer Science

Link

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

Reference34 articles.

1. Automatic differentiation in machine learning: A survey;Baydin A. G.;Journal of Machine Learning Research,2018

2. Prediction of aerodynamic flow fields using convolutional neural networks

3. Physics-informed neural networks (PINNs) for fluid mechanics: a review

4. A zero-equation turbulence model for indoor airflow simulation

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Reconstruction of ship propeller wake field based on self-adaptive loss balanced physics-informed neural networks;Ocean Engineering;2024-10

2. Numerical investigation of the operating conditions affecting nitrogen condensation on a three-dimensional wing in the cryogenic wind tunnel;Engineering Applications of Computational Fluid Mechanics;2024-08-21

3. Physics-informed neural networks (P INNs): application categories, trends and impact;International Journal of Numerical Methods for Heat & Fluid Flow;2024-07-10

4. A novel spatial–temporal prediction method for the effects of fish movement on flow field based on hybrid deep neural network;Engineering Applications of Computational Fluid Mechanics;2024-07-04

5. Enhancing the accuracy of physics-informed neural networks for indoor airflow simulation with experimental data and Reynolds-averaged Navier–Stokes turbulence model;Physics of Fluids;2024-06-01