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

Authors

1K

  1. RG
    Rommel G. Regis

    Saint Joseph's University, United States

    9 publications · 1.4K citations

    9

    h-index

  2. CA
    Christine A. Shoemaker

    Cornell University, United States

    5 publications · 773 citations

    5

    h-index

  3. YJ
    Yaochu Jin

    University of Surrey, United Kingdom

    5 publications · 416 citations

    5

    h-index

  4. TR
    Tapabrata Ray

    UNSW Sydney, Australia

    5 publications · 183 citations

    5

    h-index

  5. HW
    Handing Wang

    Xidian University, China

    4 publications · 130 citations

    4

    h-index

1–5 of 1K

Venues

  1. Structural and Multidisciplinary Optimization

    12 publications · 671 citations

    12

    h-index

  2. Engineering Optimization

    11 publications · 1.1K citations

    11

    h-index

  3. Engineering Analysis with Boundary Elements

    9 publications · 371 citations

    9

    h-index

  4. Computers & Mathematics with Applications

    8 publications · 882 citations

    8

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  5. IEEE Transactions on Cybernetics

    7 publications · 797 citations

    7

    h-index

1–5