Regensburg 2022 – wissenschaftliches Programm

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DY: Fachverband Dynamik und Statistische Physik

DY 24: Stochastic Thermodynamics and Information Processing

DY 24.5: Vortrag

Mittwoch, 7. September 2022, 10:45–11:00, H19

Thermodynamic inference in partially accessible Markov networks: A unifying perspective from transition-based waiting time distributions — •Jann van der Meer, Benjamin Ertel, and Udo Seifert — II. Institut für Theoretische Physik, Universität Stuttgart, 70550 Stuttgart, Germany

The inference of thermodynamic quantities from the description of an only partially accessible physical system is a central challenge in stochastic thermodynamics. A common approach is coarse-graining, which maps the dynamics of such a system to a reduced effective one. While coarse-graining states of the system into compound ones is a well studied concept, recent evidence hints at a complementary description by considering observable transitions and waiting times. We consider waiting time distributions between two consecutive transitions of a partially observable Markov network and their ratios to quantify irreversibility and entropy production. We formulate criteria whether this entropy estimator provides a lower bound or recovers the full physical entropy production. Additionally, we derive estimators for the topology of the network, i.e., the presence of a hidden cycle, its number of states and its driving affinity. From a different perspective, our results can be condensed into a fluctuation theorem for an equivalent semi-Markov process. This mathematical perspective provides a unifying framework to compare our and known estimators. The correct version of time-reversal is crucial to clarify the relationship of formal versus physical irreversibility. Numerical calculations illustrate our exact results and estimate the quality of our bounds.