Koopman-based Deep Learning for Nonlinear System Estimation

arXiv:2405.00627v1 Announce Type: cross
Abstract: Nonlinear differential equations are encountered as models of fluid flow, spiking neurons, and many other systems of interest in the real world. Common features of these systems are that their behaviors are difficult to describe exactly and invariably unmodeled dynamics present challenges in making precise predictions. In many cases the models exhibit extremely complicated behavior due to bifurcations and chaotic regimes. In this paper, we present a novel data-driven linear estimator that uses Koopman operator …

abstract arxiv challenges cs.lg cs.sy deep learning differential dynamics eess.sy features flow making neurons predictions systems type world