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Related papers: Dissipative timescales from coarse-graining irreve…

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By considering general Markov stochastic dynamics and its coarse-graining, we study the framework of stochastic thermodynamics for the original and reduced descriptions corresponding to different scales. We are especially concerned with the…

Statistical Mechanics · Physics 2015-01-29 Yohei Nakayama , Kyogo Kawaguchi

The entropy production rate is a central quantity in non-equilibrium statistical physics, scoring how far a stochastic process is from being time-reversible. In this paper, we compute the entropy production of diffusion processes at…

Statistical Mechanics · Physics 2023-06-19 Lancelot Da Costa , Grigorios A. Pavliotis

Many non-equilibrium, active processes are observed at a coarse-grained level, where different microscopic configurations are projected onto the same observable state. Such "lumped" observables display memory, and in many cases the…

Statistical Mechanics · Physics 2024-04-29 Kristian Blom , Kevin Song , Etienne Vouga , Aljaž Godec , Dmitrii E. Makarov

We investigate nonequilibrium steady-state dynamics in both continuous- and discrete-state stochastic processes. Our analysis focuses on planar diffusion dynamics and their coarse-grained approximations by discrete-state Markov chains.…

Statistical Mechanics · Physics 2026-05-12 Ramón Nartallo-Kaluarachchi , Renaud Lambiotte , Alain Goriely

We propose a procedure to fully reconstruct the time-dependent coefficients of convolutionless non-Markovian dissipative generators via a finite number of experimental measurements. By combining a tomography based approach with a proper…

Quantum Physics · Physics 2011-09-26 Bruno Bellomo , Antonella De Pasquale , Giulia Gualdi , Ugo Marzolino

Nonequilibrium systems, in particular living organisms, are maintained by irreversible transformations of energy that drive diverse functions. Quantifying their irreversibility, as measured by energy dissipation, is essential for…

Statistical Mechanics · Physics 2025-05-21 Qiwei Yu , Pedro E. Harunari

A computational tool for coarse-graining nonlinear systems of ordinary differential equations in time is discussed. Three illustrative model examples are worked out that demonstrate the range of capability of the method. This includes the…

Numerical Analysis · Mathematics 2017-11-23 Sabyasachi Chatterjee , Amit Acharya , Zvi Artstein

We study dynamical reversibility in stationary stochastic processes from an information theoretic perspective. Extending earlier work on the reversibility of Markov chains, we focus on finitary processes with arbitrarily long conditional…

Statistical Mechanics · Physics 2015-05-28 Christopher J. Ellison , John R. Mahoney , Ryan G. James , James P. Crutchfield , Joerg Reichardt

The coarse-graining approach to deriving the quantum Markovian master equation is revisited, with close attention given to the underlying approximations. It is further argued that the time interval over which the coarse-graining is…

Quantum Physics · Physics 2017-12-05 J D Cresser , C Facer

In this article, we analyze three classes of time-reversal of a Markov process with Gaussian noise on a manifold. We first unveil a commutativity constraint for the most general of these time-reversals to be well defined. Then we give a…

Statistical Mechanics · Physics 2024-08-09 Jérémy O'Byrne , Michael E. Cates

We present general results on fluctuations and spatial correlations of the coarse-grained empirical density and current of Markovian diffusion in equilibrium or non-equilibrium steady states on all time scales. We unravel a deep connection…

Statistical Mechanics · Physics 2023-04-06 Cai Dieball , Aljaž Godec

We examine stochastic processes that are used to model nonequilibrium processes (e.g, pulling RNA or dragging colloids) and so deliberately violate detailed balance. We argue that by combining an information-theoretic measure of…

Statistical Mechanics · Physics 2009-11-13 R. A. Blythe

We show the variational convergence of an irreversible Markov jump process describing a finite stochastic particle system to the solution of a countable infinite system of deterministic time-inhomogeneous quadratic differential equations…

Analysis of PDEs · Mathematics 2025-07-08 Jasper Hoeksema , Chun Yin Lam , André Schlichting

Irreversible behaviour is traditionally associated with open stochastic dynamical systems, but an asymmetry in the probabilistic specification of a closed deterministic system can similarly lead to a disparity between the likelihoods of a…

Quantum Physics · Physics 2018-09-03 Claudia L. Clarke , Ian J. Ford

We propose a method to measure real-valued time series irreversibility which combines two differ- ent tools: the horizontal visibility algorithm and the Kullback-Leibler divergence. This method maps a time series to a directed network…

Data Analysis, Statistics and Probability · Physics 2015-05-30 Lucas Lacasa , Ángel M. Núñez , Édgar Roldán , Juan M. R. Parrondo , Bartolo Luque

The dynamics of time-reversible systems are statistically indistinguishable when observed forward or backward in time. A rich literature of statistical methods to distinguish irreversible dynamics from the reversible dynamics of linear,…

Data Analysis, Statistics and Probability · Physics 2026-04-20 Teresa Dalle Nogare , Ben D. Fulcher

Optically confined colloidal particles, when placed in close proximity, form a dissipatively coupled system through hydrodynamic interactions. The role of such interactions influencing irreversibility and energy dissipation in…

There is a relation between the irreversibility of thermodynamic processes as expressed by the breaking of time-reversal symmetry, and the entropy production in such processes. We explain on an elementary mathematical level the relations…

Statistical Mechanics · Physics 2007-05-23 C. Maes , K. Netocny

Entropy production plays a fundamental role in the study of non-equilibrium systems by offering a quantitative handle on the degree of time-reversal symmetry breaking. It depends crucially on the degree of freedom considered as well as on…

Statistical Mechanics · Physics 2022-05-04 Luca Cocconi , Guillaume Salbreux , Gunnar Pruessner

Understanding the stability and long-time behavior of generative models is a fundamental problem in modern machine learning. This paper provides quantitative bounds on the sampling error of score-based generative models by leveraging…

Machine Learning · Statistics 2026-01-30 Stanislas Strasman , Gabriel Cardoso , Sylvain Le Corff , Vincent Lemaire , Antonio Ocello