Surrogate-based Optimization Under Uncertainty

Surrogate-Based Optimization Under Uncertainty is a computational framework within multifidelity uncertainty quantification that enables efficient decision-making under uncertainty by using hierarchical surrogate models to approximate expensive, high-fidelity simulations while explicitly accounting for model discrepancies and stochastic variability in the optimization process.

211

Publications

12K

Citations

592

Authors

256

Institutions

Publications per year

2017–2026

131

Authors

592

  1. AP
    Amir Parnianifard

    Chulalongkorn University, Thailand

    4 publications · 148 citations

    5

    h-index

  2. KW
    Karen Willcox

    Massachusetts Institute of Technology, United States

    5 publications · 1.4K citations

    5

    h-index

  3. QD
    Qingyun Duan

    Beijing Normal University, China

    4 publications · 234 citations

    4

    h-index

  4. ZD
    Zhenhua Di

    Beijing Normal University, China

    4 publications · 234 citations

    4

    h-index

  5. AJ
    Andy J. Keane

    University of Southampton, United Kingdom

    4 publications · 2.4K citations

    4

    h-index

1–5 of 592

Venues

  1. Structural and Multidisciplinary Optimization

    16 publications · 1.4K citations

    13

    h-index

  2. AIAA Journal

    9 publications · 329 citations

    9

    h-index

  3. Engineering Optimization

    6 publications · 200 citations

    6

    h-index

  4. Journal of Mechanical Design

    6 publications · 166 citations

    6

    h-index

  5. arXiv (Cornell University)

    Repository · 6 publications · 126 citations

    6

    h-index

1–5