Surrogate Error Estimation

Surrogate Error Estimation is a quantitative method used in surrogate-based optimization to assess the discrepancy between a surrogate model’s predictions and the true response of the underlying system or high-fidelity model, thereby distinguishing it from other surrogate model evaluation techniques by focusing specifically on error quantification for reliability and accuracy improvement.

44

Publications

3.8K

Citations

114

Authors

75

Institutions

Publications per year

2017–2026

17

Authors

114

  1. RT
    Raphael T. Haftka

    University of Florida, United States

    4 publications · 471 citations

    4

    h-index

  2. AM
    Achille Messac

    Rensselaer Polytechnic Institute, United States

    3 publications · 41 citations

    3

    h-index

  3. SC
    Souma Chowdhury

    University at Buffalo, State University of New York, United States

    3 publications · 41 citations

    3

    h-index

  4. AM
    Ali Mehmani

    Columbia University, United States

    3 publications · 41 citations

    3

    h-index

  5. DS
    Donna Spiegelman

    Harvard University, United States

    2 publications · 532 citations

    2

    h-index

1–5 of 114

Venues

  1. AIAA Journal

    4 publications · 1.2K citations

    4

    h-index

  2. Structural and Multidisciplinary Optimization

    3 publications · 486 citations

    3

    h-index

  3. Statistics in Medicine

    2 publications · 108 citations

    2

    h-index

  4. Biometrika

    2 publications · 200 citations

    2

    h-index

  5. Reliability Engineering & System Safety

    2 publications · 335 citations

    2

    h-index

1–5