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

doi:10.1016/j.jcp.2022.111145

IMECH-IR > 非线性力学国家重点实验室

	Artificial neural network based response surface for data-driven dimensional analysis
	Xu ZY(许昭越)1,2; Zhang XL(张鑫磊)1,2; Wang SZ(王士召)1,2 ; He GW(何国威)1,2
通讯作者	Wang, Shizhao(wangsz@lnm.imech.ac.cn)
发表期刊	JOURNAL OF COMPUTATIONAL PHYSICS
	2022-06-15
卷号	459 页码:19
ISSN	0021-9991
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.
关键词	Artificial neural network Response surface Data-driven dimensional analysis Machine learning Fluid-structure interaction
DOI	10.1016/j.jcp.2022.111145
收录类别	SCI
语种	英语
WOS记录号	WOS:000793406800008
关键词[WOS]	METHODOLOGY ; DRAG
WOS研究方向	Computer Science ; Physics
WOS类目	Computer Science, Interdisciplinary Applications ; Physics, Mathematical
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]
项目资助者	NSFC Basic Science Center Program for 'Multiscale Problems in Nonlinear Mechanics' ; National Natural Science Foundation of China
论文分区	一类/力学重要期刊
力学所作者排名	1
RpAuthor	Wang, Shizhao
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://dspace.imech.ac.cn/handle/311007/89553
专题	非线性力学国家重点实验室
作者单位	1.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China; 2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 101408, Peoples R China
推荐引用方式 GB/T 7714	Xu ZY,Zhang XL,Wang SZ,et al. Artificial neural network based response surface for data-driven dimensional analysis[J]. JOURNAL OF COMPUTATIONAL PHYSICS,2022,459:19.
APA	许昭越,张鑫磊,王士召,&何国威.(2022).Artificial neural network based response surface for data-driven dimensional analysis.JOURNAL OF COMPUTATIONAL PHYSICS,459,19.
MLA	许昭越,et al."Artificial neural network based response surface for data-driven dimensional analysis".JOURNAL OF COMPUTATIONAL PHYSICS 459(2022):19.

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