Polynomial Regression Surrogates
Polynomial Regression Surrogates is a surrogate modeling technique that approximates the input-output behavior of a computationally expensive optimization problem using a global polynomial function fitted to sampled data points.
88
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5.2K
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314
Authors
184
Institutions
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2017–2026
46
88
Linda R. Shanock, Benjamin E. Baran, William A. Gentry et al. · Journal of Business and Psychology · 2010 · 943 citations
Robert D. Laird, Andres De Los Reyes · Journal of Abnormal Child Psychology · 2012 · 314 citations
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Centre National de la Recherche Scientifique
Paris, France
3 h-index
9 publications
285 citations
Sandia National Laboratories
Albuquerque, United States
2 h-index
4 publications
160 citations
University of Illinois Chicago
Chicago, United States
2 h-index
3 publications
519 citations
University of Technology Sydney
Sydney, Australia
2 h-index
5 publications
201 citations
Argonne National Laboratory
Lemont, United States
2 h-index
4 publications
331 citations

Polytechnic University of Bari
Bari, Italy
2 h-index
2 publications
43 citations
Texas A&M University
College Station, United States
2 h-index
5 publications
200 citations

National University of Defense Technology
Changsha, China
2 h-index
9 publications
147 citations
1–8 of 184
3 publications · 192 citations
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2 publications · 265 citations
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2 publications · 153 citations
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1 publications · 943 citations
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1 publications · 39 citations
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