期刊
PHYSICA D-NONLINEAR PHENOMENA
卷 334, 期 -, 页码 82-98出版社
ELSEVIER
DOI: 10.1016/j.physd.2016.02.003
关键词
Persistent Homology; Nonlinear Dynamics; Fluid Dynamics
资金
- NSF [NSF-DMS-0835621, 0915019, 1125174, 1248071, DMS-1125302, CMMI-1234436, DMS-1125234]
- AFOSR
- DARPA
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1521771, 1125234, 0915019] Funding Source: National Science Foundation
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1248071, 1125174, 1125302] Funding Source: National Science Foundation
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-Benard 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. (C) 2016 Elsevier B.V. All rights reserved.
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