Bulletin of the American Physical Society
71st Annual Meeting of the APS Division of Fluid Dynamics
Volume 63, Number 13
Sunday–Tuesday, November 18–20, 2018; Atlanta, Georgia
Session L01: Nonlinear Dynamics: Coherent Structures II
4:05 PM–6:41 PM,
Monday, November 19, 2018
Georgia World Congress Center Room: B201
Chair: George Haller, ETH Zurich
Abstract ID: BAPS.2018.DFD.L01.8
Abstract: L01.00008 : Unsupervised machine learning for coherent structure identification*
5:36 PM–5:49 PM
Brooke E. Husic
Brooke E. Husic
Kristy L. Schlueter-Kuck
John O. Dabiri
The clustering of fluid particle trajectories into coherent structures often requires a priori assumptions about the nature of the coherent subgroups. We present a new method, simultaneous Coherent Structure Coloring (sCSC), which performs unsupervised learning on measured or simulated Lagrangian flow trajectories without anticipating the underlying structure of the data. Unlike common methods, rather than clustering similar states (i.e., particle trajectories), sCSC separates the most dissimilar states via a generalized eigenvalue problem. The set of eigenvector solutions are bifurcated and sequentially applied to the data, yielding a binary dendrogram representation. The number of coherent structures emerges naturally, since many fewer branches are occupied than is combinatorically possible. We demonstrate that sCSC can identify the structures governing fluid transport in both theoretical models and laboratory measurements with two orders of magnitude less data than existing methods and no a priori assumptions.
*This work was supported by the U.S. National Science Foundation and by the Department of Defense through the National Defense Science & Engineering Graduate Fellowship Program.
To cite this abstract, use the following reference: http://meetings.aps.org/link/BAPS.2018.DFD.L01.8
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