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Related papers: Entropy Production and Information Flow for Markov…

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D.J. Evans, et al., [Phys. Rev. Lett. 71, 2401 (1993)] discovered a relation, subsequently known as the Fluctuation Theorem (FT), which quantifies the probability of observing fluctuations violating the second law of thermodynamics in…

Statistical Mechanics · Physics 2017-05-19 A. Y. Gorobets , J. M. Borrero , S. Berdyugina

The concept of entropy in nonequilibrium macroscopic systems is investigated in the light of an extended equation of motion for the density matrix obtained in a previous study. It is found that a time-dependent information entropy can be…

Statistical Mechanics · Physics 2009-11-10 W. T. Grandy

The transfer entropy is a well-established measure of information flow, which quantifies directed influence between two stochastic time series and has been shown to be useful in a variety fields of science. Here we introduce the transfer…

Statistical Mechanics · Physics 2018-07-23 Sosuke Ito

Systems operating out of equilibrium exchange energy and matter with the environment, thus producing entropy in their surroundings. Since the entropy production depends on the current flowing throughout the system, its quantification is…

Statistical Mechanics · Physics 2019-12-13 Daniel Maria Busiello , Amos Maritan

We consider a hidden Markov model with multiple observation processes, one of which is chosen at each point in time by a policy---a deterministic function of the information state---and attempt to determine which policy minimises the…

Probability · Mathematics 2015-03-17 James Y. Zhao

It is a great challenge of nonequilibrium statistical mechanics to calculate entropy production within a microscopic theory. In the framework of linear irreversible thermodynamics, we combine the Mori-Zwanzig-Forster projection operator…

Statistical Mechanics · Physics 2014-01-28 Raphael Wittkowski , Hartmut Löwen , Helmut R. Brand

How is it that entropy derivatives almost in their own are characterizing the state of a system close to equilibrium, and what happens further away from it? We explain within the framework of Markov jump processes why fluctuation theory can…

Statistical Mechanics · Physics 2009-08-24 Christian Maes , Karel Netočný , Bram Wynants

In recent advances in finite-time thermodynamics, optimization of entropy production required for finite-time information processing is an important issue. In this work, we consider finite-time feedback processes in classical discrete…

Statistical Mechanics · Physics 2025-03-18 Rihito Nagase , Takahiro Sagawa

The Fisher-Rao metric from Information Geometry is related to phase transition phenomena in classical statistical mechanics. Several studies propose to extend the use of Information Geometry to study more general phase transitions in…

Statistical Mechanics · Physics 2016-05-04 Omri Har Shemesh , Rick Quax , Alfons G. Hoekstra , Peter M. A. Sloot

For a Markovian dynamics on discrete states, the logarithmic ratio of waiting-time distributions between two successive, instantaneous transitions in forward and backward direction is a measure of time-irreversibility. It thus serves as an…

Statistical Mechanics · Physics 2024-06-13 Ellen Meyberg , Julius Degünther , Udo Seifert

The interrelationships of the fundamental biological processes natural selection, mutation, and stochastic drift are quantified by the entropy rate of Moran processes with mutation, measuring the long-run variation of a Markov process. The…

Dynamical Systems · Mathematics 2014-01-14 Marc Harper

A unified formulation of the density functional theory is constructed on the foundations of entropic inference in both the classical and the quantum regimes. The theory is introduced as an application of entropic inference for inhomogeneous…

Statistical Mechanics · Physics 2021-12-20 Ahmad Yousefi

We derive various exact results for Markovian systems that spontaneously relax to a non-equilibrium steady-state by using joint probability distributions symmetries of different entropy production decompositions. The analytical approach is…

Statistical Mechanics · Physics 2012-02-10 Reinaldo García-García , Vivien Lecomte , A. B. Kolton , D. Domínguez

Nowadays many tools, e.g. fluctuation relations, are available to characterize the statistical properties of non-equilibrium systems. However, most of these tools rely on the assumption that the driving noise is normally distributed. Here…

Statistical Mechanics · Physics 2023-11-29 Dario Lucente , Andrea Puglisi , Massimiliano Viale , Angelo Vulpiani

Focusing on stochastic systems arising in mean-field models, the systems under consideration belong to the class of switching diffusions, in which continuous dynamics and discrete events coexist and interact. The discrete events are modeled…

Probability · Mathematics 2019-01-18 Son L. Nguyen , George Yin , Tuan A. Hoang

Continuous feedback control of Langevin processes may be non-Markovian due to a time lag between the measurement and the control action. We show that this requires to modify the basic relation between dissipation and time-reversal and to…

Statistical Mechanics · Physics 2015-06-18 T. Munakata , M. L. Rosinberg

Entropy production along a single stochastic trajectory of a biomolecule is discussed for two different sources of non-equilibrium. For a molecule manipulated mechanically by an AFM or an optical tweezer, entropy production (or…

Statistical Mechanics · Physics 2007-06-20 Tim Schmiedl , Thomas Speck , Udo Seifert

We establish a novel generalization of the fluctuation theorem for partially-masked nonequilibrium dynamics. We introduce a partial entropy production with a subset of all possible transitions, and show that the partial entropy production…

Statistical Mechanics · Physics 2015-01-20 Naoto Shiraishi , Takahiro Sagawa

Firstly the fluctuation theorems (FT) for expended work in a driven nonequilibrium system, isolated or thermostatted, together with the ensuing Jarzynski work-energy (W-E) relationships, will be discussed and reobtained. Secondly, the…

Statistical Mechanics · Physics 2016-10-31 Carolyne M. Van Vliet

Diffusion models generate high-quality synthetic data. They operate by defining a continuous-time forward process which gradually adds Gaussian noise to data until fully corrupted. The corresponding reverse process progressively "denoises"…

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