Network measures of mixing
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Network measures of mixing. / Banisch, Ralf; Koltai, Péter; Padberg-Gehle, Kathrin.
In: Chaos, Vol. 29, No. 6, 063125, 06.2019.Research output: Journal contributions › Journal articles › Research › peer-review
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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 - 2019/6
Y1 - 2019/6
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
C2 - 31266326
AN - SCOPUS:85068133690
VL - 29
JO - Chaos
JF - Chaos
SN - 1054-1500
IS - 6
M1 - 063125
ER -