A virtual model architecture for engineering structures with Twin Extended Support Vector Regression (T-X-SVR) method

Wang, Q; Wu, D; Li, G; Gao, W

A virtual model architecture for engineering structures with Twin Extended Support Vector Regression (T-X-SVR) method

Wang, Q Wu, D

Li, G Gao, W

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Computer Methods in Applied Mechanics and Engineering, 2021, 386, pp. 114121
Issue Date:: 2021-12-01

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Field	Value	Language
dc.contributor.author	Wang, Q
dc.contributor.author	Wu, D https://orcid.org/0000-0002-7284-5024
dc.contributor.author	Li, G
dc.contributor.author	Gao, W
dc.date.accessioned	2022-03-08T05:55:19Z
dc.date.available	2022-03-08T05:55:19Z
dc.date.issued	2021-12-01
dc.identifier.citation	Computer Methods in Applied Mechanics and Engineering, 2021, 386, pp. 114121
dc.identifier.issn	0045-7825
dc.identifier.uri	http://hdl.handle.net/10453/155069
dc.description.abstract	A machine learning aided virtual model architecture framework is presented. With great computational stability and preserved convexity features, the Twin Extended Support Vector Regression (T-X-SVR) method is adopted to imitate the underpinned and sophisticated constitutive relationship between the systematic inputs and outcomes in real-world applications. Various numerical simulations can be implemented on the virtual model with greatly reduced computational costs. The multi-type information from heterogeneous sources and multifarious engineering applications are supported by the proposed framework. For the virtual model aided numerical analysis with the multi-type information, fuzzy-valued probabilistic distributional characteristics of the bounds of the concerned structural responses are estimated. The capability of the modularity feature of the virtual model improves the operational flexibility which makes the implementation of such advance technique more user friendly. To demonstrate the applicability and computational efficiency of the proposed framework, three practical engineering stimulated problems (i.e., the mechanical dynamic system, multiphysics with heat transfer and gas flow interaction, and the expansion process of a biomedical stent with both material and geometry nonlinearities), involving multi-type information (i.e., random parameters and fuzzy sets), are fully investigated through the proposed virtual model architecture framework.
dc.language	en
dc.publisher	Elsevier BV
dc.relation	http://purl.org/au-research/grants/arc/IH150100006
dc.relation	http://purl.org/au-research/grants/arc/IH200100010
dc.relation	http://purl.org/au-research/grants/arc/DP210101353
dc.relation.ispartof	Computer Methods in Applied Mechanics and Engineering
dc.relation.isbasedon	10.1016/j.cma.2021.114121
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 09 Engineering
dc.subject.classification	Applied Mathematics
dc.title	A virtual model architecture for engineering structures with Twin Extended Support Vector Regression (T-X-SVR) method
dc.type	Journal Article
utslib.citation.volume	386
utslib.for	01 Mathematical Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-03-08T05:55:15Z
pubs.publication-status	Published
pubs.volume	386

Abstract:

A machine learning aided virtual model architecture framework is presented. With great computational stability and preserved convexity features, the Twin Extended Support Vector Regression (T-X-SVR) method is adopted to imitate the underpinned and sophisticated constitutive relationship between the systematic inputs and outcomes in real-world applications. Various numerical simulations can be implemented on the virtual model with greatly reduced computational costs. The multi-type information from heterogeneous sources and multifarious engineering applications are supported by the proposed framework. For the virtual model aided numerical analysis with the multi-type information, fuzzy-valued probabilistic distributional characteristics of the bounds of the concerned structural responses are estimated. The capability of the modularity feature of the virtual model improves the operational flexibility which makes the implementation of such advance technique more user friendly. To demonstrate the applicability and computational efficiency of the proposed framework, three practical engineering stimulated problems (i.e., the mechanical dynamic system, multiphysics with heat transfer and gas flow interaction, and the expansion process of a biomedical stent with both material and geometry nonlinearities), involving multi-type information (i.e., random parameters and fuzzy sets), are fully investigated through the proposed virtual model architecture framework.

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http://hdl.handle.net/10453/155069