Reduced Order Modeling of Nonlinear Dynamical Systems Using Slow Manifolds

Abstract: Model order reduction in high-dimensional, nonlinear dynamical systems if often enabled through fast-slow timescale separation. One such approach involves identifying a low-dimensional slow manifold to which the state rapidly converges and subsequently studying the behavior on the slow manifold. Leveraging the isostable coordinate framework, which considers the slowest decaying principle Koopman eigenmodes of a stable attractor, this work investigates the relationships between stable and unstable fixed points and periodic orbits and associated slow manifolds embedded in state space. It is found that under appropriate technical conditions, a slow manifold is formed by the intersection of the unstable manifold of an unstable fixed point (resp.,~periodic orbit) and the stable manifold of a stable periodic orbit (resp.,~fixed point). This insight allows for the straightforward computation of slow manifolds that enable model order reduction. Detailed examples are provided for two different highly nonlinear dynamical systems, the first being a coupled system of Hodgkin-Huxley neurons and the second being a biophysically detailed model of circadian oscillations. The resulting reduced order models are illustrated in the consideration of two different biologically motivated control objectives.