Neural Design Generation

Neural Design Generation is a computational design methodology that leverages neural network architectures to autonomously synthesize artifacts, systems, or forms by learning and generating within high-dimensional design spaces, thereby distinguishing it from rule-based or manual design processes through its reliance on data-driven, learned representations.

16

Publications

522

Citations

57

Authors

27

Institutions

Publications per year

2017–2026

10

Authors

57

  1. GT
    G. T. Barkema

    Utrecht University, Netherlands

    1 publications · 13 citations

    1

    h-index

  2. HZ
    Hao Zhang

    Southwest University, China

    1 publications · 40 citations

    1

    h-index

  3. WQ
    Weiyi Qi

    Shanghai Institute of Materia Medica, China

    1 publications · 24 citations

    1

    h-index

  4. AM
    Ali Mohammad

    Aligarh Muslim University, India

    1 publications · 10 citations

    1

    h-index

  5. ZZ
    Zhen Zhang

    University of Chinese Academy of Sciences, China

    1 publications · 10 citations

    1

    h-index

1–5 of 57

Venues

  1. Technology|Architecture + Design

    TAD · 1 publications · 38 citations

    1

    h-index

  2. Biomedical Signal Processing and Control

    1 publications · 14 citations

    1

    h-index

  3. Network Computation in Neural Systems

    1 publications · 13 citations

    1

    h-index

  4. Neurocomputing

    1 publications · 10 citations

    1

    h-index

  5. 1

    h-index

1–5