Analysis of Kolmogorov flow and Rayleigh–Bénard convection using persistent homology

Miroslav Kramár, Rachel Levanger, Jeffrey Tithof, Balachandra Suri, Mu Xu, Mark Paul, Michael F. Schatz, Konstantin Mischaikow

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

Abstract

We use persistent homology to build a quantitative understanding of large complex systems that are driven far-from-equilibrium. In particular, we analyze image time series of flow field patterns from numerical simulations of two important problems in fluid dynamics: Kolmogorov flow and Rayleigh–Bénard convection. For each image we compute a persistence diagram to yield a reduced description of the flow field; by applying different metrics to the space of persistence diagrams, we relate characteristic features in persistence diagrams to the geometry of the corresponding flow patterns. We also examine the dynamics of the flow patterns by a second application of persistent homology to the time series of persistence diagrams. We demonstrate that persistent homology provides an effective method both for quotienting out symmetries in families of solutions and for identifying multiscale recurrent dynamics. Our approach is quite general and it is anticipated to be applicable to a broad range of open problems exhibiting complex spatio-temporal behavior.

Original languageEnglish (US)
Pages (from-to)82-98
Number of pages17
JournalPhysica D: Nonlinear Phenomena
Volume334
DOIs
StatePublished - Nov 1 2016
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2016 Elsevier B.V.

Keywords

  • Fluid Dynamics
  • Nonlinear Dynamics
  • Persistent Homology

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