Multi-fidelity Optimization

Multi-Fidelity Optimization is a parametric optimization paradigm that integrates models of varying fidelity levels—ranging from low-accuracy approximations to high-accuracy simulations or empirical data—to efficiently converge to optimal solutions by strategically balancing computational cost and solution accuracy.

231

Publications

10.5K

Citations

714

Authors

259

Institutions

Publications per year

2017–2026

151

Authors

714

  1. QZ
    Qi Zhou

    Huazhong University of Science and Technology, China

    11 publications · 397 citations

    11

    h-index

  2. JJ
    Juan J. Alonso

    Stanford University, United States

    10 publications · 443 citations

    10

    h-index

  3. JX
    Jie Xu

    George Mason University, United States

    8 publications · 374 citations

    8

    h-index

  4. EH
    Edward Huang

    George Mason University, United States

    7 publications · 330 citations

    7

    h-index

  5. LH
    Loo Hay Lee

    National University of Singapore, Singapore

    7 publications · 375 citations

    7

    h-index

1–5 of 714

Venues

  1. Structural and Multidisciplinary Optimization

    20 publications · 1.3K citations

    17

    h-index

  2. arXiv (Cornell University)

    Repository · 15 publications · 515 citations

    14

    h-index

  3. Aerospace Science and Technology

    AST · 6 publications · 219 citations

    6

    h-index

  4. Engineering Optimization

    5 publications · 129 citations

    5

    h-index

  5. AIAA Journal

    5 publications · 626 citations

    5

    h-index

1–5