Performance-based Generation

Performance-Based Generation is an algorithmic form generation approach that belongs to the class of computational design methods wherein the differentiating characteristic is the use of quantifiable performance criteria—such as structural efficiency, environmental response, or functional behavior—to drive and constrain the generative process.

5

Publications

155

Citations

16

Authors

7

Institutions

Publications per year

2017–2026

2

Authors

16

  1. GP
    George Pratt

    Federal Mogul (United States), United States

    1 publications · 27 citations

    1

    h-index

  2. HM
    Hossein Moradian

    Chosun University, South Korea

    1 publications · 11 citations

    1

    h-index

  3. JJ
    Jerome J. Connor

    Massachusetts Institute of Technology, United States

    1 publications · 21 citations

    1

    h-index

  4. KT
    Kar‐Ann Toh

    Yonsei University, South Korea

    1 publications · 17 citations

    1

    h-index

  5. SJ
    Shulei Ji

    Xi'an Jiaotong University, China

    1 publications · 79 citations

    1

    h-index

1–5 of 16

Venues

  1. European Journal of High Ability

    1 publications · 27 citations

    1

    h-index

  2. arXiv (Cornell University)

    Repository · 1 publications · 79 citations

    1

    h-index

  3. Pattern Recognition

    1 publications · 17 citations

    1

    h-index

  4. Journal of Electronic Testing

    JETTA · 1 publications · 11 citations

    1

    h-index

  5. Research in Engineering Design

    1 publications · 21 citations

    1

    h-index

1–5