Network measures of mixing

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Network measures of mixing. / Banisch, Ralf; Koltai, Péter; Padberg-Gehle, Kathrin.

in: Chaos, Band 29, Nr. 6, 063125, 2019.

Publikationen: Beiträge in ZeitschriftenZeitschriftenaufsätze

Harvard

Banisch, R, Koltai, P & Padberg-Gehle, K 2019, 'Network measures of mixing' Chaos, Bd 29, Nr. 6, 063125. DOI: 10.1063/1.5087632

APA

Banisch, R., Koltai, P., & Padberg-Gehle, K. (2019). Network measures of mixing. Chaos, 29(6), [063125]. DOI: 10.1063/1.5087632

Vancouver

Banisch R, Koltai P, Padberg-Gehle K. Network measures of mixing. Chaos. 2019;29(6). 063125. Erhältlich von, DOI: 10.1063/1.5087632

Bibtex

@article{708eeeb6be8543f6a41eb41bd44536fd,
title = "Network measures of mixing",
abstract = "Transport and mixing processes in fluid flows can be studied directly from Lagrangian trajectory data, such as those 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.",
keywords = "Mathematics",
author = "Ralf Banisch and P{\'e}ter Koltai and Kathrin Padberg-Gehle",
note = "This work is supported by the Deutsche Forschungsgemeinschaft (DFG) through the Priority Programme SPP 1881 “Turbulent Superstructures.” P.K. also acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG) through the Collaborative Research Center 1114 “Scaling Cascades of Complex Systems,” Project A01. K.P.G. also acknowledges funding from EU Marie-Skłodowska-Curie ITN Critical Transitions in Complex Systems (H2020-MSCA-2014-ITN 643073 CRITICS).",
year = "2019",
doi = "10.1063/1.5087632",
language = "English",
volume = "29",
journal = "Chaos",
issn = "1054-1500",
publisher = "American Institute of Physics",
number = "6",

}

RIS

TY - JOUR

T1 - Network measures of mixing

AU - Banisch,Ralf

AU - Koltai,Péter

AU - Padberg-Gehle,Kathrin

N1 - This work is supported by the Deutsche Forschungsgemeinschaft (DFG) through the Priority Programme SPP 1881 “Turbulent Superstructures.” P.K. also acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG) through the Collaborative Research Center 1114 “Scaling Cascades of Complex Systems,” Project A01. K.P.G. also acknowledges funding from EU Marie-Skłodowska-Curie ITN Critical Transitions in Complex Systems (H2020-MSCA-2014-ITN 643073 CRITICS).

PY - 2019

Y1 - 2019

N2 - Transport and mixing processes in fluid flows can be studied directly from Lagrangian trajectory data, such as those 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.

AB - Transport and mixing processes in fluid flows can be studied directly from Lagrangian trajectory data, such as those 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.

KW - Mathematics

UR - http://www.scopus.com/inward/record.url?scp=85068133690&partnerID=8YFLogxK

U2 - 10.1063/1.5087632

DO - 10.1063/1.5087632

M3 - Journal articles

VL - 29

JO - Chaos

T2 - Chaos

JF - Chaos

SN - 1054-1500

IS - 6

M1 - 063125

ER -

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