Local Causal States and Discrete Coherent Structures

TL;DR

This paper introduces a formal, behavior-driven theory for identifying and analyzing coherent structures in discrete dynamical systems using local causal states, enabling unsupervised discovery without explicit equations.

Contribution

It generalizes computational mechanics to a local spatiotemporal setting, providing a novel method to uncover coherent structures in fully-discrete systems.

Findings

Successfully applied to elementary cellular automata

Compared results with dynamic-invariant-set approach

Demonstrated unsupervised identification of structures

Abstract

Coherent structures form spontaneously in nonlinear spatiotemporal systems and are found at all spatial scales in natural phenomena from laboratory hydrodynamic flows and chemical reactions to ocean, atmosphere, and planetary climate dynamics. Phenomenologically, they appear as key components that organize the macroscopic behaviors in such systems. Despite a century of effort, they have eluded rigorous analysis and empirical prediction, with progress being made only recently. As a step in this, we present a formal theory of coherent structures in fully-discrete dynamical field theories. It builds on the notion of structure introduced by computational mechanics, generalizing it to a local spatiotemporal setting. The analysis' main tool employs the \localstates, which are used to uncover a system's hidden spatiotemporal symmetries and which identify coherent structures as spatially-localized deviations from those symmetries. The approach is behavior-driven in the sense that it does not rely on directly analyzing spatiotemporal equations of motion, rather it considers only the spatiotemporal fields a system generates. As such, it offers an unsupervised approach to discover and describe coherent structures. We illustrate the approach by analyzing coherent structures generated by elementary cellular automata, comparing the results with an earlier, dynamic-invariant-set approach that decomposes fields into domains, particles, and particle interactions.