Data-Driven Operator Theoretic Methods for Phase Space Learning and Analysis

Abstract

This paper uses data-driven operator theoretic approaches to explore the global phase space of a dynamical system. We defined conditions for discovering new invariant subsets in the state space of a dynamical system starting from an invariant subset based on the spectral properties of the Koopman operator. When the system evolution is known locally in several invariant subsets in the state space of a dynamical system, a phase space stitching result is derived that yields the global Koopman operator. Additionally, in the case of equivariant systems, a phase space stitching result is developed to identify the global Koopman operator using the symmetry properties between the invariant subsets of the dynamical system and time-series data from any one of the invariant subsets. Finally, these results are extended to topologically conjugate dynamical systems; in particular, the relation between the Koopman tuple of topologically conjugate systems is established. The proposed results are demonstrated on several second-order nonlinear dynamical systems including a bistable toggle switch. Our method elucidates a strategy for designing discovery experiments: experiment execution can be done in many steps, and models from different invariant subsets can be combined to approximate the global Koopman operator.