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.
Bibliographical noteFunding Information:
The work of MK, RL, and KM has been partially supported by NSF grants NSF-DMS-0835621 , 0915019 , 1125174 , 1248071 , and contracts from AFOSR and DARPA . The work of JRF, BS and MFS has been partially supported by NSF grants DMS-1125302 , CMMI-1234436 . The work of MRP and MX has been supported by NSF grant DMS-1125234 .
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- Fluid Dynamics
- Nonlinear Dynamics
- Persistent Homology