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.
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Publications
174
Citations
25
Authors
9
Institutions
Publications per year
2017–2026
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How realistic are the wakes of scaled wind turbine models?
Chengyu Wang, Filippo Campagnolo, Helena Canét et al. · Wind energy science · 2021 · 46 citations · Full text
Generation of intense magnetic wakes by relativistic laser pulses in plasma
Marcel Lamač, Uddhab Chaulagain, J. Nejdl et al. · Scientific Reports · 2023 · 16 citations · Full text
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George Washington University
Washington, United States
1 h-index
5 publications
90 citations
Technical University of Munich
Munich, Germany
1 h-index
5 publications
230 citations
Swedish e-Science Research Centre
Stockholm, Sweden
1 h-index
3 publications
87 citations

Delft University of Technology
Delft, The Netherlands
1 h-index
4 publications
48 citations

KTH Royal Institute of Technology
Stockholm, Sweden
1 h-index
3 publications
87 citations

École Normale Supérieure Paris-Saclay
Gif-sur-Yvette, France
1 h-index
1 publications
29 citations
University of Michigan
Ann Arbor, United States
1 h-index
2 publications
32 citations
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)
Cologne, Germany
1 h-index
3 publications
39 citations
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1 publications · 13 citations
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1 publications · 12 citations
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Conference · 1 publications · 18 citations
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WES · 1 publications · 46 citations
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Repository · 1 publications · 40 citations
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