Local Surrogate Models

Local Surrogate Models is a type of surrogate model that approximates the behavior of a complex, computationally expensive objective function within a restricted region of the design space, enabling efficient local optimization through simpler, rapidly evaluable representations.

156

Publications

9.1K

Citations

536

Authors

257

Institutions

Publications per year

2017–2026

84

Authors

536

  1. YO
    Yew-Soon Ong

    Nanyang Technological University, Singapore

    6 publications · 1.5K citations

    6

    h-index

  2. YJ
    Yaochu Jin

    University of Surrey, United Kingdom

    4 publications · 600 citations

    4

    h-index

  3. PB
    Prasanth B. Nair

    University of Southampton, United Kingdom

    4 publications · 1.2K citations

    4

    h-index

  4. MP
    Markus P. Rumpfkeil

    University of Dayton, United States

    4 publications · 87 citations

    4

    h-index

  5. PB
    Philip Beran

    United States Air Force Research Laboratory, United States

    3 publications · 52 citations

    3

    h-index

1–5 of 536

Venues

  1. AIAA Journal

    7 publications · 745 citations

    7

    h-index

  2. arXiv (Cornell University)

    Repository · 6 publications · 162 citations

    6

    h-index

  3. IEEE Transactions on Cybernetics

    6 publications · 801 citations

    6

    h-index

  4. IEEE Transactions on Evolutionary Computation

    6 publications · 659 citations

    6

    h-index

  5. Structural and Multidisciplinary Optimization

    4 publications · 896 citations

    4

    h-index

1–5