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Combinatorial models of global dynamics: learning cycling motion from data
1 : Department of Mathematics, Technical University of Munich
(TUM)
-
Website
Boltzmannstrasse 3 D-85747 Garching -
Germany
2 : Rutgers, The State University of New Jersey [New Brunswick]
(RUTGERS)
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Website
100 George Street, New Brunswick, NJ 08901 -
United States
3 : Boston Consulting Group
(BCG)
* : Corresponding author
London -
United Kingdom
We describe a computational method for constructing a coarse combinatorial model of a dynamical system in which the macroscopic states are given by elementary cycling motions of the system. Our method is based on tools from topological data analysis and can be applied to time series data. We illustrate the construction by a perturbed double well Hamiltonian as well as the Lorenz system.
