Neural Generative Design

Neural Generative Design is a computational design methodology that synthesizes generative models with neural architectures to autonomously produce novel forms or structures through learned implicit design principles and data-driven exploration.

8

Publications

334

Citations

41

Authors

11

Institutions

Publications per year

2017–2026

8

Authors

41

  1. JH
    Jianjun Hu

    University of South Carolina, United States

    3 publications · 70 citations

    3

    h-index

  2. YS
    Yuqi Song

    University of South Carolina, United States

    3 publications · 70 citations

    3

    h-index

  3. LW
    Lai Wei

    University of South Carolina, United States

    2 publications · 47 citations

    2

    h-index

  4. EM
    Edirisuriya M. Dilanga Siriwardane

    University of South Carolina, United States

    2 publications · 51 citations

    2

    h-index

  5. NF
    Nihang Fu

    University of South Carolina, United States

    2 publications · 47 citations

    2

    h-index

1–5 of 41

Venues

  1. Applied Optics

    AO · 1 publications · 23 citations

    1

    h-index

  2. Buildings

    1 publications · 46 citations

    1

    h-index

  3. Advanced Science

    1 publications · 28 citations

    1

    h-index

  4. Journal of Cheminformatics

    1 publications · 19 citations

    1

    h-index

  5. Molecular Informatics

    1 publications · 42 citations

    1

    h-index

1–5