Surrogate-based Optimization Under Uncertainty
Surrogate-Based Optimization Under Uncertainty is a computational framework within multifidelity uncertainty quantification that enables efficient decision-making under uncertainty by using hierarchical surrogate models to approximate expensive, high-fidelity simulations while explicitly accounting for model discrepancies and stochastic variability in the optimization process.
211
Publications
12K
Citations
592
Authors
256
Institutions
Publications per year
2017–2026
131
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Northwestern Polytechnical University
Xi'an, China
9 h-index
33 publications
942 citations

Centre National de la Recherche Scientifique
Paris, France
7 h-index
12 publications
462 citations
Massachusetts Institute of Technology
Cambridge, United States
7 h-index
10 publications
1.8K citations
University of Southampton
Southampton, United Kingdom
7 h-index
12 publications
5K citations
Georgia Institute of Technology
Atlanta, United States
6 h-index
8 publications
171 citations
Dalian University of Technology
Dalian, China
6 h-index
22 publications
476 citations
University of Florida
Gainesville, United States
5 h-index
12 publications
399 citations

Beijing Normal University
Beijing, China
5 h-index
25 publications
1.7K citations
1–8 of 256
16 publications · 1.4K citations
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9 publications · 329 citations
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6 publications · 200 citations
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6 publications · 166 citations
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Repository · 6 publications · 126 citations
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