Regression Surrogates

Regression Surrogates is a surrogate modeling approach that employs regression techniques to construct a data-driven approximation of a complex computational model by mapping input parameters to output responses through statistical learning.

128

Publications

8.2K

Citations

365

Authors

181

Institutions

Publications per year

2017–2026

64

Variants

Regression-based Surrogates

Authors

365

  1. RJ
    Raymond J. Carroll

    Texas A&M University, United States

    5 publications · 459 citations

    5

    h-index

  2. IC
    Ivo Couckuyt

    Ghent University, Belgium

    4 publications · 469 citations

    4

    h-index

  3. RT
    Raphael T. Haftka

    University of Florida, United States

    4 publications · 489 citations

    4

    h-index

  4. TD
    Tom Dhaene

    Ghent University, Belgium

    4 publications · 469 citations

    4

    h-index

  5. FV
    Felipe Viana

    University of Central Florida, United States

    4 publications · 489 citations

    4

    h-index

1–5 of 365

Venues

  1. Structural and Multidisciplinary Optimization

    8 publications · 796 citations

    8

    h-index

  2. 5

    h-index

  3. arXiv (Cornell University)

    Repository · 5 publications · 234 citations

    5

    h-index

  4. 4

    h-index

  5. Information Sciences

    3 publications · 106 citations

    3

    h-index

1–5