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By applying dimensionality reduction to surrogate models, less computation is required to generate surrogate model parts while retaining sufficient representation accuracy of the full process. This paper aims to review the current literature on dimensionality reduction integrated with surrogate modeling methods. A review of the current state-of-the-art dimensionality reduction and surrogate modeling methods is introduced with a discussion of their mathematical implications, applications, and limitations. Finally, current studies that combine the two topics are discussed and avenues of further research are presented.<\/jats:p>","DOI":"10.1007\/s41019-022-00193-5","type":"journal-article","created":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T17:02:42Z","timestamp":1661101362000},"page":"402-427","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":104,"title":["Dimensionality Reduction in Surrogate Modeling: A Review of Combined Methods"],"prefix":"10.1007","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3286-3129","authenticated-orcid":false,"given":"Chun Kit Jeffery","family":"Hou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1873-9837","authenticated-orcid":false,"given":"Kamran","family":"Behdinan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,21]]},"reference":[{"key":"193_CR1","doi-asserted-by":"publisher","unstructured":"Narisetty (2020) Bayesian model selection for high-dimensional data. 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