Modified screening-based Kriging method with cross validation and application to engineering design

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dc.contributor.authorKang, Kyeonghwanko
dc.contributor.authorQin, Caiyanko
dc.contributor.authorLee, Bong Jaeko
dc.contributor.authorLee, Ikjinko
dc.date.accessioned2019-03-19T01:48:57Z-
dc.date.available2019-03-19T01:48:57Z-
dc.date.created2019-03-09-
dc.date.created2019-03-09-
dc.date.issued2019-06-
dc.identifier.citationAPPLIED MATHEMATICAL MODELLING, v.70, pp.626 - 642-
dc.identifier.issn0307-904X-
dc.identifier.urihttp://hdl.handle.net/10203/251756-
dc.description.abstractIn this paper, a basis screening Kriging method using cross validation error is proposed to alleviate computational burden of the dynamic Kriging while maintaining its accuracy. Metamodeling is widely used for design optimization of complex engineering applications where considerable computation time is required. The Kriging method is one of popular metamodeling methods due to its accuracy and efficiency. There have been many attempts to improve accuracy of the Kriging method, and the dynamic Kriging method using cross-validation error, which selects adequate basis functions to best describe the mean structure of a response using a genetic algorithm, achieves outstanding performance in terms of accuracy. However, despite its accuracy, the dynamic Kriging requires very large amounts of computation because of the genetic algorithm and no limitation for order of basis functions. In the proposed method, a basis function set is determined by screening each basis function instead of using the genetic algorithm, which has advantages in computation for high dimensional metamodels or repeated metamodel generation. Numerical studies with four mathematical examples and two engineering applications verify that the proposed basis screening Kriging significantly reduces computation time with similar accuracy as the dynamic Kriging.-
dc.languageEnglish-
dc.publisherELSEVIER SCIENCE INC-
dc.titleModified screening-based Kriging method with cross validation and application to engineering design-
dc.typeArticle-
dc.identifier.wosid000468714000035-
dc.identifier.scopusid2-s2.0-85061442892-
dc.type.rimsART-
dc.citation.volume70-
dc.citation.beginningpage626-
dc.citation.endingpage642-
dc.citation.publicationnameAPPLIED MATHEMATICAL MODELLING-
dc.identifier.doi10.1016/j.apm.2019.01.030-
dc.contributor.localauthorLee, Bong Jae-
dc.contributor.localauthorLee, Ikjin-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorMetamodel-
dc.subject.keywordAuthorSurrogate model-
dc.subject.keywordAuthorKriging-
dc.subject.keywordAuthorCross validation-
dc.subject.keywordPlusSPATIAL INTERPOLATION-
dc.subject.keywordPlusCOMPUTER EXPERIMENTS-
dc.subject.keywordPlusREGRESSION-
dc.subject.keywordPlusAPPROXIMATION-
dc.subject.keywordPlusMODEL-
dc.subject.keywordPlusOPTIMIZATION-
dc.subject.keywordPlusSURROGATES-
dc.subject.keywordPlusPREDICTION-
dc.subject.keywordPlusLIKELIHOOD-
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