Radial Basis Function Surrogates
Radial Basis Function Surrogates is a surrogate modeling technique that constructs an approximation of a complex objective function by linearly combining radial basis functions centered at sampled data points, distinguished by its reliance on distance-based basis functions to interpolate or predict responses in optimization tasks.
349
Publications
16.8K
Citations
1K
Authors
432
Institutions
Publications per year
2017–2026
145
Variants
Rbf Surrogates
349
1–5 of 349
1K
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432
Saint Joseph's University
Philadelphia, United States
8 h-index
9 publications
1.1K citations

Huazhong University of Science and Technology
Wuhan, China
8 h-index
26 publications
1.5K citations
Dalian University of Technology
Dalian, China
6 h-index
19 publications
814 citations

Northwestern Polytechnical University
Xi'an, China
6 h-index
18 publications
958 citations

Uppsala University
Uppsala, Sweden
6 h-index
14 publications
1.1K citations

Central South University
Changsha, China
6 h-index
12 publications
848 citations
National University of Singapore
Singapore, Singapore
5 h-index
10 publications
341 citations
Mississippi State University
Starkville, United States
5 h-index
10 publications
617 citations
1–8 of 432
12 publications · 671 citations
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11 publications · 1.1K citations
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9 publications · 371 citations
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8 publications · 882 citations
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7 publications · 797 citations
7
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