Speaker
Description
Entropy production arguably is the most universal quantifier for non-equilibrium systems in contact with a thermal bath of well-defined temperature. If not all driven degrees of freedom are accessible, it can typically not be inferred exactly from experimental data. One goal of thermodynamic inference is to derive at least a model-free lower bound from such data. The thermodynamic uncertainty relation with its generalization to time-dependent driving is the most prominent example to date as I will first recall in this talk. Even better bounds can be achieved by using waiting-time distributions and the novel concepts of Markovian events and Markovian snippets that allow a thermodynamically consistent identification of entropy production along individual coarse-grained trajectories [1-3]. As an illustration I will show an application to experimental data on unfolding of a small peptide [4].
[1] J. van der Meer, B. Ertel, and U. Seifert, PRX 12, 031025, 2022
[2] J. van der Meer, J. Degünther, and U. Seifert, PRL 130, 257101, 2023
[3] J. Degünther, J. van der Meer, and U. Seifert, PRR 6, 023175, 2024
[4] J. Degünther, J. van der Meer, and U. Seifert, PNAS 121, e2405371121, 2024
