Inference of entropy production for periodically driven systems
Pedro E. Harunari, Carlos E. Fiore, and Andre C. Barato

TL;DR
This paper develops a method to estimate entropy production in periodically driven stochastic systems using transition statistics, extending previous steady-state approaches and highlighting the importance of probability currents.
Contribution
It introduces a new entropy production estimation technique for periodically driven systems that does not require tracking the protocol, unlike previous steady-state methods.
Findings
The method estimates entropy production based on transition statistics and waiting times.
Emergence of probability currents is necessary for meaningful entropy production estimates.
The approach is validated on a molecular pump model, linking physical parameters to entropy production.
Abstract
The problem of estimating entropy production from incomplete information in stochastic thermodynamics is essential for theory and experiments. Whereas a considerable amount of work has been done on this topic, arguably, most of it is restricted to the case of nonequilibrium steady states driven by a fixed thermodynamic force. Based on a recent method that has been proposed for nonequilibrium steady states, we obtain an estimate of the entropy production based on the statistics of visible transitions and their waiting times for the case of periodically driven systems. The time-dependence of transition rates in periodically driven systems produces several differences in relation to steady states, which is reflected in the entropy production estimation. More specifically, we propose an estimate that does depend on the time between transitions but is independent of the specific time of the…
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Taxonomy
TopicsAdvanced Thermodynamics and Statistical Mechanics
