Berlin 2024 – scientific programme

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DY: Fachverband Dynamik und Statistische Physik

DY 23: Stochastic Thermodynamics

DY 23.2: Talk

Wednesday, March 20, 2024, 09:45–10:00, BH-N 128

An estimator of entropy production for partially accessible Markov networks based on the observation of blurred transitions — •Benjamin Ertel and Udo Seifert — II. Institut für Theoretische Physik, Universität Stuttgart, 70550 Stuttgart, Germany

A central task in stochastic thermodynamics is the estimation of entropy production for partially accessible Markov networks as these models correspond to the partial observation of real-world systems. We establish an effective transition-based description for partially accessible Markov networks with transitions that are not distinguishable and therefore blurred for an external observer. We demonstrate that, in contrast to the description of fully resolved transitions, this effective description is non-Markovian at any point in time. We derive an information-theoretic bound for this non-ideal observation scenario which reduces to an operationally accessible entropy estimator under specific conditions that are met by a broad class of systems. We illustrate the operational relevance of this system class and the quality of the corresponding entropy estimator based on the numerical analysis of various representative examples.

Keywords: stochastic thermodynamics; non-Markovianity; estimation of entropy production