Spatial Constraint Optimization

Spatial Constraint Optimization is a mathematical modeling framework that distinguishes itself by systematically integrating excluded volume effects into the optimization of spatial configurations, ensuring that physical non-overlap and geometric accessibility constraints are explicitly preserved in predictive simulations of molecular or particulate systems.

54

Publications

2.2K

Citations

186

Authors

111

Institutions

Publications per year

2017–2026

21

Authors

186

  1. SH
    Siewert Hugelier

    Université de Lille, France

    2 publications · 55 citations

    2

    h-index

  2. CR
    Cyril Ruckebusch

    Centre National de la Recherche Scientifique, France

    2 publications · 55 citations

    2

    h-index

  3. DF
    Dean F. Wong

    Johns Hopkins University, United States

    2 publications · 239 citations

    2

    h-index

  4. YZ
    Yun Zhou

    Johns Hopkins University, United States

    2 publications · 239 citations

    2

    h-index

  5. KK
    Kelby K. Chan

    University of California, Los Angeles, United States

    1 publications · 17 citations

    1

    h-index

1–5 of 186

Venues

  1. IEEE Transactions on Image Processing

    3 publications · 169 citations

    3

    h-index

  2. NeuroImage

    3 publications · 275 citations

    3

    h-index

  3. Neurocomputing

    2 publications · 32 citations

    2

    h-index

  4. Sensors

    2 publications · 112 citations

    2

    h-index

  5. 2

    h-index

1–5