Robust Surrogate Modeling

Robust Surrogate Modeling is a computational modeling approach that belongs to the class of surrogate modeling techniques, distinguished by its explicit incorporation of uncertainty quantification and robustness criteria to ensure reliable performance predictions under input variability and model approximation errors.

80

Publications

4.3K

Citations

251

Authors

129

Institutions

Publications per year

2017–2026

44

Authors

251

  1. TD
    Tom Dhaene

    Ghent University, Belgium

    2 publications · 32 citations

    2

    h-index

  2. XS
    Xueguan Song

    Dalian University of Technology, China

    2 publications · 113 citations

    2

    h-index

  3. PS
    Prashant Singh

    Ghent University, Belgium

    2 publications · 32 citations

    2

    h-index

  4. YC
    Ying Cao

    University of California, Berkeley, United States

    2 publications · 47 citations

    2

    h-index

  5. RC
    Rajib Chowdhury

    Indian Institute of Technology Roorkee, India

    2 publications · 96 citations

    2

    h-index

1–5 of 251

Venues

  1. IEEE Transactions on Magnetics

    4 publications · 136 citations

    4

    h-index

  2. AIAA Journal

    3 publications · 510 citations

    3

    h-index

  3. Structural and Multidisciplinary Optimization

    3 publications · 84 citations

    3

    h-index

  4. Journal of Mechanical Design

    3 publications · 251 citations

    3

    h-index

  5. International Journal of Hydrogen Energy

    2 publications · 29 citations

    2

    h-index

1–5