Adaptive Form Generation

Adaptive Form Generation is an algorithmic method within computational design that iteratively synthesizes structural or spatial configurations by dynamically responding to contextual constraints and performance criteria through feedback-driven refinement processes.

6

Publications

134

Citations

12

Authors

5

Institutions

Publications per year

2017–2026

1

Authors

12

  1. MJ
    Magesh Jayapandian

    University of Michigan, United States

    2 publications · 62 citations

    2

    h-index

  2. HV
    H. V. Jagadish

    AT&T (United States), United States

    1 publications · 40 citations

    1

    h-index

  3. ID
    Isabel Damas‐Moreira

    Universidade do Porto, Portugal

    1 publications · 39 citations

    1

    h-index

  4. MF
    Martin Frank

    Leibniz University Hannover, Germany

    1 publications · 11 citations

    1

    h-index

  5. PS
    Peter Saunders

    Imperial College London, United Kingdom

    1 publications · 11 citations

    1

    h-index

1–5 of 12

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 1 publications · 40 citations

    1

    h-index

  2. Engineering With Computers

    1 publications · 11 citations

    1

    h-index

  3. Leonardo

    1 publications · 11 citations

    1

    h-index

  4. Knowledge-Based Systems

    1 publications · 11 citations

    1

    h-index

  5. Current Opinion in Behavioral Sciences

    1 publications · 39 citations

    1

    h-index

1–5