When you can't count, sample! Computable entropies beyond equilibrium from basin volumes

TL;DR

This paper reviews recent methods for estimating entropy in non-equilibrium systems by sampling basin volumes, addressing challenges and highlighting progress in computing high-dimensional state spaces.

Contribution

It provides a comprehensive overview of recent techniques for directly estimating entropy beyond equilibrium, emphasizing basin volume computation in non-equilibrium systems.

Findings

Recent methods enable entropy estimation in non-equilibrium systems

Progress in high-dimensional basin volume computation

Identification of challenges and future directions

Abstract

In statistical mechanics, measuring the number of available states and their probabilities, and thus the system's entropy, enables the prediction of the macroscopic properties of a physical system at equilibrium. This predictive capacity hinges on the knowledge of the a priori probabilities of observing the states of the system, given by the Boltzmann distribution. Unfortunately, the successes of equilibrium statistical mechanics are hard to replicate out of equilibrium, where the a priori probabilities of observing states are in general not known, precluding the na\"ive application of usual tools. In the last decade, exciting developments have occurred that enable the direct numerical estimation of the entropy and density of states of athermal and non-equilibrium systems, thanks to significant methodological advances in the computation of the volume of high-dimensional basins of attraction. Here, we provide a detailed account of these methods, underscoring the challenges that lie in such estimations, recent progress on the matter, and promising directions for future work.