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	Derivation of heterogeneous material laws via data-driven principal component expansions
	Yang, Hang; Guo, Xu; Tang, Shan; Liu, Wing Kam
刊名	COMPUTATIONAL MECHANICS
	2019
卷号	64页码:365-379
关键词	Computational data-driven Artificial neural network 3D objective material laws Principal strain and stress space Engineering structure with microstructure
ISSN号	0178-7675
URL标识	查看原文
WOS记录号	[DB:DC_IDENTIFIER_WOSID]
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/3231546
专题	大连理工大学
作者单位	1.Dalian Univ Technol, Dept Engn Mech, State Key Lab Struct Anal Ind Equipment, Dalian 116023, Peoples R China. 2.Dalian Univ Technol, Dept Engn Mech, State Key Lab Struct Anal Ind Equipment, Dalian 116023, Peoples R China. 3.Dalian Univ Technol, Int Res Ctr Computat Mech, Dalian 116023, Peoples R China. 4.Northwestern Univ, Dept Mech Engn, Evanston, IL 60208 USA.
推荐引用方式 GB/T 7714	Yang, Hang,Guo, Xu,Tang, Shan,et al. Derivation of heterogeneous material laws via data-driven principal component expansions[J]. COMPUTATIONAL MECHANICS,2019,64:365-379.
APA	Yang, Hang,Guo, Xu,Tang, Shan,&Liu, Wing Kam.(2019).Derivation of heterogeneous material laws via data-driven principal component expansions.COMPUTATIONAL MECHANICS,64,365-379.
MLA	Yang, Hang,et al."Derivation of heterogeneous material laws via data-driven principal component expansions".COMPUTATIONAL MECHANICS 64(2019):365-379.

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