Lagrangian Feature Extraction
Lagrangian Feature Extraction is a methodological framework in flow feature extraction that identifies and tracks coherent structures by following individual fluid particles over time, distinguished from Eulerian approaches through its particle-centric, trajectory-based analysis.
12
Publications
496
Citations
49
Authors
29
Institutions
Publications per year
2017–2026
7
Parents
12
Hidden Geometry of Ocean Flows
Carolina Mendoza, Ana M. Mancho · Physical Review Letters · 2010 · 165 citations · Full text
Wenbo Tang, Pak Wai Chan, George Haller · Chaos An Interdisciplinary Journal of Nonlinear Science · 2010 · 52 citations
An advanced hybrid deep adversarial autoencoder for parameterized nonlinear fluid flow modelling
Meiling Cheng, F. Fang, Christopher C. Pain et al. · Computer Methods in Applied Mechanics and Engineering · 2020 · 45 citations · Full text
Computing, Mechanical Engineering, Computational Fluid Dynamics +10
1–5 of 12
49
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29

Universidad Autónoma de Madrid
Madrid, Spain
3 h-index
6 publications
438 citations

Zuse Institute Berlin
Berlin, Germany
3 h-index
9 publications
372 citations
Institute of Mathematical Sciences
Madrid, Spain
2 h-index
5 publications
411 citations
Florida State University
Tallahassee, United States
2 h-index
2 publications
60 citations

Universidad de Alcalá
Alcalá de Henares, Spain
2 h-index
2 publications
54 citations

Universidad Carlos III de Madrid
Madrid, Spain
2 h-index
5 publications
411 citations

University of Bristol
Bristol, United Kingdom
2 h-index
8 publications
216 citations
Arizona State University
Tempe, United States
1 h-index
1 publications
52 citations
1–8 of 29
TVCG · 2 publications · 81 citations
2
h-index
2 publications · 52 citations
2
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1 publications · 52 citations
1
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1 publications · 45 citations
1
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Repository · 1 publications · 27 citations
1
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1–5