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Artificial neural network based response surface for data-driven dimensional analysis

文献类型：期刊论文

作者Xu, Zhaoyue1,2; Zhang, Xinlei1,2; Wang, Shizhao1,2; He, Guowei1,2; He GW(何国威); Wang SZ(王士召)
刊名JOURNAL OF COMPUTATIONAL PHYSICS
出版日期2022-06-15
卷号459页码:19
ISSN号0021-9991
关键词Artificial neural network Response surface Data-driven dimensional analysis Machine learning Fluid-structure interaction
DOI10.1016/j.jcp.2022.111145
通讯作者Wang, Shizhao(wangsz@lnm.imech.ac.cn)
英文摘要The classical dimensional analysis method has limitations in determining the uniqueness and relative importance of the dimensionless quantities. A machine-learning based dimensional analysis method is proposed to address the limitations. The proposed method identifies unique and relevant dimensionless quantities by combining an artificial neural network with the data-driven dimensional analysis. We employ a fully connected neural network to construct the ridge function for the response surface in a physical system. The gradient of the response surface for active subspace analysis is computed based on a finite difference approximation. An effective approach is proposed to determine the independent variables of experimental measurements or numerical simulations for computing the gradient of the response surface. The proposed method is validated by analyzing benchmark pipe flows and a fluid-structure interaction system. The dominant dimensionless quantities obtained by the proposed method are consistent with those reported in the literature. The proposed method has the advantage of identifying the relatively important dimensionless quantities without referring to the complex theoretical equations. (C)& nbsp;2022 Elsevier Inc. All rights reserved.
WOS关键词METHODOLOGY ; DRAG
资助项目NSFC Basic Science Center Program for 'Multiscale Problems in Nonlinear Mechanics'[11988102] ; National Natural Science Foundation of China[11922214] ; National Natural Science Foundation of China[91752118] ; National Natural Science Foundation of China[91952301]
WOS研究方向Computer Science ; Physics
语种英语
WOS记录号WOS:000793406800008
资助机构NSFC Basic Science Center Program for 'Multiscale Problems in Nonlinear Mechanics' ; National Natural Science Foundation of China
源URL[http://dspace.imech.ac.cn/handle/311007/89552]  
专题力学研究所_非线性力学国家重点实验室
通讯作者Wang, Shizhao
作者单位1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 101408, Peoples R China
2.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Xu, Zhaoyue,Zhang, Xinlei,Wang, Shizhao,et al. Artificial neural network based response surface for data-driven dimensional analysis[J]. JOURNAL OF COMPUTATIONAL PHYSICS,2022,459:19.
APA Xu, Zhaoyue,Zhang, Xinlei,Wang, Shizhao,He, Guowei,何国威,&王士召.(2022).Artificial neural network based response surface for data-driven dimensional analysis.JOURNAL OF COMPUTATIONAL PHYSICS,459,19.
MLA Xu, Zhaoyue,et al."Artificial neural network based response surface for data-driven dimensional analysis".JOURNAL OF COMPUTATIONAL PHYSICS 459(2022):19.

入库方式： OAI收割

来源：力学研究所

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