08 Fakultät Mathematik und Physik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/9
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Item Open Access On stochastic thermodynamics under incomplete information : thermodynamic inference from Markovian events(2024) Meer, Jann van der; Seifert, Udo (Prof. Dr.)Item Open Access Inferring kinetics and entropy production from observable transitions in partially accessible, periodically driven Markov networks(2024) Maier, Alexander M.; Degünther, Julius; Meer, Jann van der; Seifert, UdoFor a network of discrete states with a periodically driven Markovian dynamics, we develop an inference scheme for an external observer who has access to some transitions. Based on waiting-time distributions between these transitions, the periodic probabilities of states connected by these observed transitions and their time-dependent transition rates can be inferred. Moreover, the smallest number of hidden transitions between accessible ones and some of their transition rates can be extracted. We prove and conjecture lower bounds on the total entropy production for such periodic stationary states. Even though our techniques are based on generalizations of known methods for steady states, we obtain original results for those as well.Item Open Access Waiting time distributions in hybrid models of motor-bead assays: a concept and tool for inference(2023) Ertel, Benjamin; Meer, Jann van der; Seifert, UdoItem Open Access Thermodynamic inference in partially accessible Markov networks: a unifying perspective from transition-based waiting time distributions(2022) Meer, Jann van der; Ertel, Benjamin; Seifert, UdoThe 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. In this work, we consider waiting time distributions between two consecutive transitions of a partially observable Markov network. We formulate an entropy estimator using their ratios to quantify irreversibility. Depending on the complexity of the underlying network, we formulate criteria to infer whether the entropy estimator recovers the full physical entropy production or whether it just provides a lower bound that improves on established results. This conceptual approach, which is based on the irreversibility of underlying cycles, additionally enables us to derive estimators for the topology of the network, i.e., the presence of a hidden cycle, its number of states, and its driving affinity. Adopting an equivalent semi-Markov description, our results can be condensed into a fluctuation theorem for the corresponding semi-Markov process. This mathematical perspective provides a unifying framework for the entropy estimators considered here and established earlier ones. The crucial role of the correct version of time reversal helps to clarify a recent debate on the meaning of formal versus physical irreversibility. Extensive numerical calculations based on a direct evaluation of waiting time distributions illustrate our exact results and provide an estimate on the quality of the bounds for affinities of hidden cycles.Item Open Access Fluctuating entropy production on the coarse-grained : inference and localization of irreversibility(2024) Degünther, Julius; Meer, Jann van der; Seifert, Udo