Multi-fidelity Optimization
Multi-Fidelity Optimization is a parametric optimization paradigm that integrates models of varying fidelity levels—ranging from low-accuracy approximations to high-accuracy simulations or empirical data—to efficiently converge to optimal solutions by strategically balancing computational cost and solution accuracy.
231
Publications
10.5K
Citations
714
Authors
259
Institutions
Publications per year
2017–2026
151
231
1–5 of 231
714
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259

Huazhong University of Science and Technology
Wuhan, China
13 h-index
59 publications
2.1K citations
Stanford University
Stanford, United States
11 h-index
41 publications
1.8K citations
George Mason University
Fairfax, United States
9 h-index
30 publications
1.4K citations
National University of Singapore
Singapore, Singapore
8 h-index
13 publications
606 citations
Georgia Institute of Technology
Atlanta, United States
7 h-index
19 publications
1.3K citations

Reykjavík University
Reykjavik, Iceland
7 h-index
7 publications
309 citations
University of Michigan
Ann Arbor, United States
7 h-index
14 publications
651 citations

Northwestern Polytechnical University
Xi'an, China
6 h-index
27 publications
666 citations
1–8 of 259
20 publications · 1.3K citations
17
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Repository · 15 publications · 515 citations
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AST · 6 publications · 219 citations
6
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5 publications · 129 citations
5
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5 publications · 626 citations
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