Permutation entropy of indexed ensembles: Quantifying thermalization dynamics

TL;DR

This paper introduces PI-Entropy, a computationally efficient measure based on permutation entropy, to quantify thermalization and mixing in complex dynamical systems, correlating with thermodynamic entropy.

Contribution

The paper presents PI-Entropy as a novel, efficient proxy for thermodynamic entropy, capable of distinguishing system time scales and tracking information loss during relaxation to equilibrium.

Findings

PI-Entropy closely tracks thermodynamic entropy during system relaxation.

Universal S-shaped relaxation pattern observed in chaotic systems.

PI-Entropy behavior varies with chaos strength in the Chirikov Standard Map.

Abstract

We introduce `PI-Entropy' $\Pi(\tilde{\rho})$ (the Permutation entropy of an Indexed ensemble) to quantify mixing due to complex dynamics for an ensemble $\rho$ of different initial states evolving under identical dynamics. We find that $\Pi(\tilde{\rho})$ acts as an excellent proxy for the thermodynamic entropy $S(\rho)$ but is much more computationally efficient. We study 1-D and 2-D iterative maps and find that $\Pi(\tilde{\rho})$ dynamics distinguish a variety of system time scales and track global loss of information as the ensemble relaxes to equilibrium. There is a universal S-shaped relaxation to equilibrium for generally chaotic systems, and this relaxation is characterized by a \emph{shuffling} timescale that correlates with the system's Lyapunov exponent. For the Chirikov Standard Map, a system with a mixed phase space where the chaos grows with nonlinear kick strength $K$, we find that for high $K$, $\Pi(\tilde{\rho})$ behaves like the uniformly hyperbolic 2-D Cat Map. For low $K$ we see periodic behavior with a relaxation envelope resembling those of the chaotic regime, but with frequencies that depend on the size and location of the initial ensemble in the mixed phase space as well as $K$. We discuss how $\Pi(\tilde{\rho})$ adapts to experimental work and its general utility in quantifying how complex systems change from a low entropy to a high entropy state.