Banisch, Ralf and Koltai, Péter and Padberg-Gehle, Kathrin
(2019)
*Network measures of mixing.*
Chaos: An Interdisciplinary Journal of Nonlinear Science 29, 063125 (2019), 29
.
pp. 1-16.

Full text not available from this repository.

Official URL: https://doi.org/10.1137/19M1261791

## Abstract

Transport and mixing processes in �uid �ows 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 coe�cient to �ow structures. In particular, we use these localmeasures to divide the family of trajectories into groups of similar dynamical behavior via manifold learning methods.

Item Type: | Article |
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Subjects: | Mathematical and Computer Sciences Mathematical and Computer Sciences > Mathematics Mathematical and Computer Sciences > Mathematics > Applied Mathematics |

Divisions: | Department of Mathematics and Computer Science > Institute of Mathematics |

ID Code: | 2465 |

Deposited By: | Monika Drueck |

Deposited On: | 14 Sep 2020 14:13 |

Last Modified: | 18 Apr 2023 07:55 |

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