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• W3100658280 abstract "Transport and mixing processes in fluid flows can be studied directly from Lagrangian trajectory data, such as obtained from particle tracking experiments. Recent work in this context highlights the application of graph-based approaches, where trajectories serve as nodes and some similarity or distance measure between them is employed to build a (possibly weighted) network, which is then analyzed using spectral methods. Here, we consider the simplest case of an unweighted, undirected network and analytically relate local network measures such as node degree or clustering coefficient to flow structures. In particular, we use these local measures to divide the family of trajectories into groups of similar dynamical behavior via manifold learning methods." @default.
• W3100658280 created "2020-11-23" @default.
• W3100658280 creator A5009219938 @default.
• W3100658280 creator A5021278553 @default.
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• W3100658280 date "2019-06-01" @default.
• W3100658280 modified "2023-10-12" @default.
• W3100658280 title "Network measures of mixing" @default.
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• W3100658280 doi "https://doi.org/10.1063/1.5087632" @default.
• W3100658280 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31266326" @default.
• W3100658280 hasPublicationYear "2019" @default.
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