Regensburg 2025 – scientific programme

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DY: Fachverband Dynamik und Statistische Physik

DY 22: Poster: Statistical Physics

DY 22.19: Poster

Wednesday, March 19, 2025, 10:00–12:00, P3

Stroboscopic measurements in Markov networks: Thermodynamic inference vs. exact generator reconstruction — •Malena Thea Bauer¹, Udo Seifert¹, and Jann van der Meer² — ¹II. Institut für Theoretische Physik, Universität Stuttgart, 70550 Stuttgart, Germany — ²Kyoto University, Graduate School of Science, Division of Physics and Astronomy, Oiwakecho 145-10, Kyoto 606-8224, Japan

A major goal of stochastic thermodynamics is to estimate the inevitable dissipation that accompanies particular observable phenomena in an otherwise not fully accessible system. Quantitative results are often formulated as lower bounds on the total entropy production, which capture a part of the total dissipation that can be determined based on the available data alone. In this work, we discuss the case of a continuous-time dynamics on a Markov network that is observed stroboscopically, i.e., at discrete points in time in regular intervals. We compare the standard approach of deriving a lower bound on the entropy production rate in the steady state to the less common method of reconstructing the generator from the observed propagators by taking the matrix logarithm. Provided that the timescale of the stroboscopic measurements is smaller than a critical value that can be determined from the available data, this latter method is able to recover all thermodynamic quantities like entropy production or cycle affinities and is therefore superior to the usual approach of deriving lower bounds. We conclude the comparison of both methods with numerical illustrations and a discussion of the requirements and limitations of both methods.

Keywords: stochastic thermodynamics; thermodynamic inference; generator reconstruction; entropy estimation; discrete time