Polynomial Regression Surrogates

Polynomial Regression Surrogates is a surrogate modeling technique that approximates the input-output behavior of a computationally expensive optimization problem using a global polynomial function fitted to sampled data points.

88

Publications

5.2K

Citations

314

Authors

184

Institutions

Publications per year

2017–2026

46

Authors

314

  1. ZL
    Zhen Luo

    University of Technology Sydney, Australia

    2 publications · 91 citations

    2

    h-index

  2. AG
    Anthony Giunta

    Sandia National Laboratories, United States

    2 publications · 84 citations

    2

    h-index

  3. JW
    Jinglai Wu

    University of Technology Sydney, Australia

    2 publications · 91 citations

    2

    h-index

  4. MA
    Mihai Anitescu

    Argonne National Laboratory, United States

    2 publications · 153 citations

    2

    h-index

  5. TC
    T.F. Cootes

    University of Manchester, United Kingdom

    2 publications · 66 citations

    2

    h-index

1–5 of 314

Venues

  1. Applied Energy

    3 publications · 192 citations

    3

    h-index

  2. IEEE Transactions on Image Processing

    2 publications · 265 citations

    2

    h-index

  3. Nuclear Science and Engineering

    2 publications · 153 citations

    2

    h-index

  4. Journal of Business and Psychology

    1 publications · 943 citations

    1

    h-index

  5. Neural Networks

    1 publications · 39 citations

    1

    h-index

1–5