Abstract

Extracting cycles from spatiotemporal data and coherent sets across multiple dynamic regimes

Gary Froyland
University of New South Wales

I will discuss two recent publications that are relevant for reconstructing network dynamics from data. The first of these is concerned with finding large-scale approximate cycles in dynamical data. These ideas are illustrated by finding the dominant cyclic behaviour in the Lorenz flow, and by producing an improved characterisation of the El-Nino Southern Oscillation from spatiotemporal data. The second part of the discussion addresses the data-driven identification of coherent (or more predictable) regions in the phase space of a dynamical system. In particular, I will discuss the difficult, but common, situation where there are several regime changes in the dynamics that cause the emergence or destruction of coherence. Each of these aspects, cyclicity and coherence, helps to reconstruct a time-varying picture of dynamics.