Berlin 2024 – scientific programme

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DY: Fachverband Dynamik und Statistische Physik

DY 31: Poster: Statistical Physics

DY 31.9: Poster

Wednesday, March 20, 2024, 15:00–18:00, Poster C

Thermodynamic inference in partially accessible, periodically driven Markov networks using transition-based waiting time distributions — •Alexander Maier, Julius Degünther, and Udo Seifert — II. Institut für Theoretische Physik, Universität Stuttgart, 70550 Stuttgart, Germany

Infering information on the dynamics including thermodynamic quantities of an only partially accessible physical system is one of the challenges of stochastic thermodynamics. In this work, we consider distributions of waiting times between consecutive detectable transitions in partially accessible, periodically driven Markov networks. These distributions allow us to infer dynamical properties like the period of the driving and time-dependent transition rates as well as thermodynamic quantities like estimators of the entropy production rate. Moreover, we conjecture a lower bound of the entropy production rate that is operationally accessible for arbitrary periodic driving.

Keywords: Thermodynamic inference; Waiting time distribution; Periodically driven Markov network; Entropy production rate