2019/08/31 by Jason J. Bramburger, J. Nathan Kutz
Mathematics · Physics and Astronomy · #math.DS #nlin.CD
paper · pdf · doi:10.1016/j.physd.2020.132479
published as Physica D 408 (2020) 132479
arxiv created 2020/03/18 · arxiv updated 2020/04/10
Poincaré maps are an integral aspect to our understanding and analysis of nonlinear dynamical systems. Despite this fact, the construction of these maps remains elusive and is primarily left to simple motivating examples. In this manuscript we propose a method of data-driven discovery of Poincaré maps based upon sparse regression techniques, specifically the sparse identification of nonlinear dynamics (SINDy) algorithm. This work can be used to determine the dynamics on and near invariant manifolds of a given dynamical system, as well as provide long-time forecasting of the coarse-grained dynamics of multiscale systems. Moreover, the method provides a mathematical formalism for determining nonlinear Floquet theory for the stability of nonlinear periodic orbits. The methods are applied to a range of examples including both ordinary and partial differential equations that exhibit periodic, quasi-periodic, and chaotic behavior.