Banisch, Ralf and Koltai, Péter and Padberg-Gehle, Kathrin
(2019)
*Network measures of mixing.*
Chaos 29, 29
.
pp. 1-15.

PDF
13MB |

Official URL: doi: 10.1063/1.5087632

## Abstract

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 coeffient 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.

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

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

ID Code: | 2590 |

Deposited By: | Monika Drueck |

Deposited On: | 27 Aug 2021 13:21 |

Last Modified: | 25 Oct 2021 20:38 |

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