Generative Shape Optimization

Generative Shape Optimization is a computational design methodology that belongs to the class of generative modeling techniques, distinguished by its integration of performance-driven objectives and iterative morphological refinement to autonomously synthesize optimal geometric configurations in response to defined constraints and functional criteria.

13

Publications

491

Citations

34

Authors

13

Institutions

Publications per year

2017–2026

10

Authors

34

  1. MF
    Mark Fuge

    University of Maryland, College Park, United States

    2 publications · 133 citations

    2

    h-index

  2. BL
    Baotong Li

    Xi'an Jiaotong University, China

    2 publications · 27 citations

    2

    h-index

  3. JH
    Jun Hong

    Xi'an Jiaotong University, China

    2 publications · 27 citations

    2

    h-index

  4. JW
    Jiyuan Wu

    Southeast University, China

    1 publications · 36 citations

    1

    h-index

  5. AH
    Amir Hooshmand

    Institute for Advanced Study, Germany

    1 publications · 11 citations

    1

    h-index

1–5 of 34

Venues

  1. Structural and Multidisciplinary Optimization

    1 publications · 10 citations

    2

    h-index

  2. AIAA Scitech 2019 Forum

    1 publications · 102 citations

    1

    h-index

  3. Automation in Construction

    1 publications · 39 citations

    1

    h-index

  4. AIAA Scitech 2020 Forum

    1 publications · 81 citations

    1

    h-index

  5. Journal of Mechanical Design

    1 publications · 17 citations

    1

    h-index

1–5