Combining lower bounds on entropy production in complex systems with multiple interacting components

TL;DR

This paper reviews how traditional stochastic thermodynamics results can be strengthened for complex systems with multiple interacting components by applying bounds to subsets of the system, leading to more powerful aggregate results.

Contribution

It introduces a method to combine bounds on entropy production for subsystems to derive stronger results for the entire complex system.

Findings

Enhanced bounds on entropy production for complex systems.

Method for combining subsystem results to improve overall thermodynamic limits.

Applicable to systems with many interacting components.

Abstract

The past two decades have seen a revolution in statistical physics, generalizing it to apply to systems of arbitrary size, evolving while arbitrarily far from equilibrium. Many of these new results are based on analyzing the dynamics of the entropy of a system that is evolving according to a Markov process. These results comprise a sub-field called ``stochastic thermodynamics''. Some of the most powerful results in stochastic thermodynamics were traditionally concerned with single, monolithic systems, evolving by themselves, ignoring any internal structure of those systems. In this chapter I review how in complex systems, composed of many interacting constituent systems, it is possible to substantially strengthen many of these traditional results of stochastic thermodynamics. This is done by ``mixing and matching'' those traditional results, to each apply to only a subset of the interacting systems, thereby producing a more powerful result at the level of the aggregate, complex system.