Neural Pattern Synthesis

Neural Pattern Synthesis is a generative pattern design method that employs neural network architectures to algorithmically generate structured visual or spatial patterns through learned representations, distinguished by its reliance on deep learning models to synthesize novel, coherent patterns from implicit or explicit design principles.

20

Publications

1.7K

Citations

79

Authors

31

Institutions

Publications per year

2017–2026

8

Authors

79

  1. AS
    Angela Scholze

    Heidelberg University, Germany

    1 publications · 15 citations

    1

    h-index

  2. SA
    Saeed Afshar

    Western Sydney University, Australia

    1 publications · 81 citations

    1

    h-index

  3. DA
    David A. Waring

    University of California, San Francisco, United States

    1 publications · 19 citations

    1

    h-index

  4. ZG
    Zijin Gu

    Cornell University, United States

    1 publications · 28 citations

    1

    h-index

  5. MN
    Maxwell Nye

    Massachusetts Institute of Technology, United States

    1 publications · 32 citations

    1

    h-index

1–5 of 79

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 313 citations

    3

    h-index

  2. Development

    3 publications · 463 citations

    3

    h-index

  3. NeuroImage

    1 publications · 28 citations

    1

    h-index

  4. Quarterly of Applied Mathematics

    QAM · 1 publications · 11 citations

    1

    h-index

  5. Frontiers in Neuroscience

    1 publications · 81 citations

    1

    h-index

1–5