Regression Surrogates
Regression Surrogates is a surrogate modeling approach that employs regression techniques to construct a data-driven approximation of a complex computational model by mapping input parameters to output responses through statistical learning.
128
Publications
8.2K
Citations
365
Authors
181
Institutions
Publications per year
2017–2026
64
Variants
Regression-based Surrogates
Parents
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365
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181
University of Florida
Gainesville, United States
5 h-index
10 publications
1K citations

Northwestern Polytechnical University
Xi'an, China
5 h-index
12 publications
674 citations
Texas A&M University
College Station, United States
5 h-index
7 publications
491 citations

Pennsylvania State University
State College, United States
4 h-index
8 publications
2K citations

University of Georgia
Athens, United States
4 h-index
6 publications
94 citations

University of Notre Dame
Notre Dame, United States
4 h-index
11 publications
287 citations

Centre National de la Recherche Scientifique
Paris, France
4 h-index
9 publications
458 citations
University of Arizona
Tucson, United States
4 h-index
6 publications
116 citations
1–8 of 181
8 publications · 796 citations
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5 publications · 453 citations
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Repository · 5 publications · 234 citations
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4 publications · 131 citations
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3 publications · 106 citations
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