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dc.contributor.authorKianifar, Mohammed R.
dc.contributor.authorCampean, Felician
dc.date.accessioned2019-07-29T10:28:35Z
dc.date.accessioned2019-08-06T09:26:32Z
dc.date.available2019-07-29T10:28:35Z
dc.date.available2019-08-06T09:26:32Z
dc.date.issued2020
dc.identifier.citationKianifar MR and Campean F (2020) Performance evaluation of metamodelling methods for engineering problems: towards a practitioner guide. Structural and Multidisciplinary Optimization. 61: 159-186.en_US
dc.identifier.urihttp://hdl.handle.net/10454/17192
dc.descriptionYesen_US
dc.description.abstractMetamodelling or surrogate modelling techniques are frequently used across the engineering disciplines in conjunction with expensive simulation models or physical experiments. With the proliferation of metamodeling techniques developed to provide enhanced performance for specific problems, and the wide availability of a diverse choice of tools in engineering software packages, the engineering task of selecting a robust metamodeling technique for practical problems is still a challenge. This research introduces a framework for describing the typology of engineering problems, in terms of dimensionality and complexity, and the modelling conditions, reflecting the noisiness of the signals and the affordability of sample sizes, and on this basis presents a systematic evaluation of the performance of frequently used metamodeling techniques. A set of metamodeling techniques, selected based on their reported use for engineering problems (i.e. Polynomial, Radial Basis Function, and Kriging), were systematically evaluated in terms of accuracy and robustness against a carefully assembled set of 18 test functions covering different types of problems, sampling conditions and noise conditions. A set of four real-world engineering case studies covering both computer simulation and physical experiments were also analysed as validation tests for the proposed guidelines. The main conclusions drawn from the study are that Kriging model with Matérn 5/2 correlation function performs consistently well across different problem types with smooth (i.e. not noisy) data, while Kriging model with Matérn 3/2 correlation function provides robust performance under noisy conditions, except for the very high noise conditions, where the Kriging model with nugget appears to provide better models. These results provide engineering practitioners with a guide for the choice of a metamodeling technique for problem types and modelling conditions represented in the study, whereas the evaluation framework and benchmarking problems set will be useful for researchers conducting similar studies.en_US
dc.language.isoenen_US
dc.publisherSpringer
dc.rights© The Author(s) 2019. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en_US
dc.subjectMetamodellingen_US
dc.subjectKrigingen_US
dc.subjectRadial basis functionsen_US
dc.subjectPolynomialsen_US
dc.subjectCorrelation functionen_US
dc.subjectKernel functionsen_US
dc.subjectResponse surfacesen_US
dc.titlePerformance evaluation of metamodelling methods for engineering problems: towards a practitioner guideen_US
dc.status.refereedYesen_US
dc.date.Accepted2019-06-30
dc.date.application2019-07-27
dc.typeArticleen_US
dc.type.versionPublished versionen_US
dc.identifier.doihttps://doi.org/10.1007/s00158-019-02352-1
dc.date.updated2019-07-29T09:28:39Z
refterms.dateFOA2019-08-06T09:27:26Z


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