Generative Design Optimization

Generative Design Optimization is a computational design methodology within the field of design optimization that systematically generates and evaluates a wide range of design alternatives through algorithmic processes, distinguished by its integration of generative algorithms, performance simulations, and constraint-driven parametric modeling to autonomously explore and optimize topological and functional configurations.

196

Publications

11.7K

Citations

690

Authors

246

Institutions

Publications per year

2017–2026

168

Authors

690

  1. LC
    Luisa Caldas

    University of California, Berkeley, United States

    4 publications · 106 citations

    4

    h-index

  2. JH
    Juejun Hu

    Massachusetts Institute of Technology, United States

    3 publications · 234 citations

    3

    h-index

  3. SA
    Sensong An

    University of Massachusetts Lowell, United States

    3 publications · 234 citations

    3

    h-index

  4. HZ
    Hualiang Zhang

    University of Massachusetts Lowell, United States

    3 publications · 234 citations

    3

    h-index

  5. CF
    Clayton Fowler

    University of Massachusetts Lowell, United States

    3 publications · 234 citations

    3

    h-index

1–5 of 690

Venues

  1. Journal of Mechanical Design

    14 publications · 844 citations

    14

    h-index

  2. Automation in Construction

    6 publications · 202 citations

    6

    h-index

  3. arXiv (Cornell University)

    Repository · 5 publications · 119 citations

    5

    h-index

  4. Energy and Buildings

    5 publications · 258 citations

    5

    h-index

  5. 4

    h-index

1–5