Recent advances and applications of surrogate models for finite element method computations: a review

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dc.contributor.authorKůdela, Jakubcs
dc.contributor.authorMatoušek, Radomilcs
dc.coverage.issue1cs
dc.coverage.volume26cs
dc.date.accessioned2024-03-11T15:46:14Z
dc.date.available2024-03-11T15:46:14Z
dc.date.issued2022-07-17cs
dc.description.abstractThe utilization of surrogate models to approximate complex systems has recently gained increased popularity. Because of their capability to deal with black-box problems and lower computational requirements, surrogates were successfully utilized by researchers in various engineering and scientific fields. An efficient use of surrogates can bring considerable savings in computational resources and time. Since literature on surrogate modelling encompasses a large variety of approaches, the appropriate choice of a surrogate remains a challenging task. This review discusses significant publications where surrogate modelling for finite element method-based computations was utilized. We familiarize the reader with the subject, explain the function of surrogate modelling, sampling and model validation procedures, and give a description of the different surrogate types. We then discuss main categories where surrogate models are used: prediction, sensitivity analysis, uncertainty quantification, and surrogate-assisted optimization, and give detailed account of recent advances and applications. We review the most widely used and recently developed software tools that are used to apply the discussed techniques with ease. Based on a literature review of 180 papers related to surrogate modelling, we discuss major research trends, gaps, and practical recommendations. As the utilization of surrogate models grows in popularity, this review can function as a guide that makes surrogate modelling more accessible.en
dc.formattextcs
dc.format.extent13709-13733cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationSOFT COMPUTING. 2022, vol. 26, issue 1, p. 13709-13733.en
dc.identifier.doi10.1007/s00500-022-07362-8cs
dc.identifier.issn1432-7643cs
dc.identifier.orcid0000-0002-4372-2105cs
dc.identifier.orcid0000-0002-3142-0900cs
dc.identifier.other178561cs
dc.identifier.researcheridP-7327-2018cs
dc.identifier.researcheridJ-3692-2015cs
dc.identifier.scopus56769626500cs
dc.identifier.scopus56180904400cs
dc.identifier.urihttps://hdl.handle.net/11012/245272
dc.language.isoencs
dc.publisherSpringercs
dc.relation.ispartofSOFT COMPUTINGcs
dc.relation.urihttps://link.springer.com/article/10.1007/s00500-022-07362-8cs
dc.rights(C) Springercs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1432-7643/cs
dc.subjectSurrogate modelen
dc.subjectSurrogate-assisted optimizationen
dc.subjectSensitivity analysisen
dc.subjectUncertainty quantificationen
dc.subjectFinite element methoden
dc.titleRecent advances and applications of surrogate models for finite element method computations: a reviewen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionacceptedVersionen
sync.item.dbidVAV-178561en
sync.item.dbtypeVAVen
sync.item.insts2024.03.11 16:46:14en
sync.item.modts2024.03.11 16:13:37en
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Ústav automatizace a informatikycs
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