Wake Scaling

Wake Scaling is a methodological framework within the study of scale effects that characterizes how the performance improvements of machine learning models with increasing data or model size deviate from expected power-law scaling due to the emergence of qualitatively new behaviors, capabilities, or structural changes in the training dynamics.

7

Publications

174

Citations

25

Authors

9

Institutions

Publications per year

2017–2026

4

Authors

25

  1. HC
    Helena Canét

    Technical University of Munich, Germany

    1 publications · 46 citations

    1

    h-index

  2. MR
    Mahsa Rahromostaqim

    University of Southern California, United States

    1 publications · 18 citations

    1

    h-index

  3. CF
    Carlos Ferreira

    Delft University of Technology, Netherlands

    1 publications · 12 citations

    1

    h-index

  4. MN
    Masaki Nakagawa

    Tokyo University of Agriculture and Technology, Japan

    1 publications · 16 citations

    1

    h-index

  5. EK
    Elektra Kleusberg

    Swedish e-Science Research Centre, Sweden

    1 publications · 29 citations

    1

    h-index

1–5 of 25

Venues

  1. Experiments in Fluids

    1 publications · 13 citations

    1

    h-index

  2. Renewable Energy

    1 publications · 12 citations

    1

    h-index

  3. 32nd AIAA Applied Aerodynamics Conference

    Conference · 1 publications · 18 citations

    1

    h-index

  4. Wind energy science

    WES · 1 publications · 46 citations

    1

    h-index

  5. Digital Collections of Colorado (Colorado State University)

    Repository · 1 publications · 40 citations

    1

    h-index

1–5