{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T07:32:02Z","timestamp":1761291122852,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,24]],"date-time":"2021-12-24T00:00:00Z","timestamp":1640304000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 Marie Sk\u0142odowska-Curie","award":["764547"],"award-info":[{"award-number":["764547"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Hybrid simulation is a method used to investigate the dynamic response of a system subjected to a realistic loading scenario. The system under consideration is divided into multiple individual substructures, out of which one or more are tested physically, whereas the remaining are simulated numerically. The coupling of all substructures forms the so-called hybrid model. Although hybrid simulation is extensively used across various engineering disciplines, it is often the case that the hybrid model and related excitation are conceived as being deterministic. However, associated uncertainties are present, whilst simulation deviation, due to their presence, could be significant. In this regard, global sensitivity analysis based on Sobol\u2019 indices can be used to determine the sensitivity of the hybrid model response due to the presence of the associated uncertainties. Nonetheless, estimation of the Sobol\u2019 sensitivity indices requires an unaffordable amount of hybrid simulation evaluations. Therefore, surrogate modeling techniques using machine learning data-driven regression are utilized to alleviate this burden. This study extends the current global sensitivity analysis practices in hybrid simulation by employing various different surrogate modeling methodologies as well as providing comparative results. In particular, polynomial chaos expansion, Kriging and polynomial chaos Kriging are used. A case study encompassing a virtual hybrid model is employed, and hybrid model response quantities of interest are selected. Their respective surrogates are developed, using all three aforementioned techniques. The Sobol\u2019 indices obtained utilizing each examined surrogate are compared with each other, and the results highlight potential deviations when different surrogates are used.<\/jats:p>","DOI":"10.3390\/make4010001","type":"journal-article","created":{"date-parts":[[2021,12,24]],"date-time":"2021-12-24T08:38:46Z","timestamp":1640335126000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Comparison of Surrogate Modeling Techniques for Global Sensitivity Analysis in Hybrid Simulation"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9388-0593","authenticated-orcid":false,"given":"Nikolaos","family":"Tsokanas","sequence":"first","affiliation":[{"name":"ETH Zurich, IBK, D-BAUG, Stefano-Franscini Platz 5, 8093 Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7188-9789","authenticated-orcid":false,"given":"Roland","family":"Pastorino","sequence":"additional","affiliation":[{"name":"Siemens Industry Software NV, Interleuvenlaan 68, 3001 Leuven, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1713-1977","authenticated-orcid":false,"given":"Bo\u017eidar","family":"Stojadinovi\u0107","sequence":"additional","affiliation":[{"name":"ETH Zurich, IBK, D-BAUG, Stefano-Franscini Platz 5, 8093 Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"127","DOI":"10.3389\/fbuil.2020.00127","article-title":"Robust Model Predictive Control for Dynamics Compensation in Real-Time Hybrid Simulation","volume":"6","author":"Tsokanas","year":"2020","journal-title":"Front. 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