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We study a class of Markov processes with finite state space and continuous time that have product form stationary distributions. We obtain a number of examples that can generate conjectures for diffusions with inert drift.

Probability · Mathematics 2008-10-19 Krzysztof Burdzy , David White

The rate of entropy production by a stochastic process quantifies how far it is from thermodynamic equilibrium. Equivalently, entropy production captures the degree to which detailed balance and time-reversal symmetry are broken. Despite…

Statistical Mechanics · Physics 2020-12-02 Luca Cocconi , Rosalba Garcia-Millan , Zigan Zhen , Bianca Buturca , Gunnar Pruessner

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…

Statistical Mechanics · Physics 2025-03-17 Malena T. Bauer , Udo Seifert , Jann van der Meer

A diffusive system coupled to unequal boundary reservoirs reaches a non-equilibrium steady state. While the full-counting-statistics of current fluctuations in these states are well understood for generic systems, results for steady-state…

Statistical Mechanics · Physics 2026-01-29 Soumyabrata Saha , Tridib Sadhu

Cross-sectional observations from a dynamical system can be modeled via steady-state distributions of Markov processes. The major challenge is then to determine whether the process parameters can be identified and estimated from the…

Statistics Theory · Mathematics 2026-03-19 Cecilie Olesen Recke , Niels Richard Hansen

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…

It is well known, mainly because of the work of Kurtz, that density dependent Markov chains can be approximated by sets of ordinary differential equations (ODEs) when their indexing parameter grows very large. This approximation cannot…

Performance · Computer Science 2014-04-04 Marco Beccuti , Enrico Bibbona , Andras Horvath , Roberta Sirovich , Alessio Angius , Gianfranco Balbo

The selection of an equilibrium state by maximising the entropy of a system, subject to certain constraints, is often powerfully motivated as an exercise in logical inference, a procedure where conclusions are reached on the basis of…

Statistical Mechanics · Physics 2015-12-03 Ian J. Ford

A variety of enhanced statistical and numerical methods are now routinely used to extract comprehensible and relevant thermodynamic information from the vast amount of complex, high-dimensional data obtained from intensive molecular…

Soft Condensed Matter · Physics 2020-10-14 Francois Sicard , Vladimir Koskin , Alessia Annibale , Edina Rosta

Mathematical modelling of biological population dynamics often involves proposing high fidelity discrete agent-based models that capture stochasticity and individual-level processes. These models are often considered in conjunction with an…

Dynamical Systems · Mathematics 2023-12-20 Daniel J. VandenHeuvel , Pascal R. Buenzli , Matthew J. Simpson

Stochastic thermodynamics allows us to define heat and work for microscopic systems far from thermodynamic equilibrium, based on observations of their stochastic dynamics. However, a complete account of the energetics necessitates that all…

Statistical Mechanics · Physics 2021-08-31 Jannik Ehrich

It is known that state-dependent, multi-step Lyapunov bounds lead to greatly simplified verification theorems for stability for large classes of Markov chain models. This is one component of the "fluid model" approach to stability of…

Optimization and Control · Mathematics 2012-05-18 Serdar Yüksel , Sean P. Meyn

Stochastic thermodynamics provides the framework to analyze thermodynamic laws and quantities along individual trajectories of small but fully observable systems. If the observable level fails to capture all relevant degrees of freedom,…

Statistical Mechanics · Physics 2024-07-01 Julius Degünther , Jann van der Meer , Udo Seifert

Semi-Markov processes play an important role in the effective description of partially accessible systems in stochastic thermodynamics. They occur, for instance, in coarse-graining procedures such as state lumping and when analyzing waiting…

Statistical Mechanics · Physics 2026-01-19 Alexander M. Maier , Jonas H. Fritz , Udo Seifert

We investigate the nonequilibrium stationary states of systems consisting of chemical reactions among molecules of several chemical species. To this end we introduce and develop a stochastic formulation of nonequilibrium thermodynamics of…

Statistical Mechanics · Physics 2018-07-04 Tânia Tomé , Mário J. de Oliveira

Denoising diffusion probabilistic models and score-matching models have proven to be very powerful for generative tasks. While these approaches have also been applied to the generation of discrete graphs, they have, so far, relied on…

Machine Learning · Computer Science 2023-08-17 Kilian Konstantin Haefeli , Karolis Martinkus , Nathanaël Perraudin , Roger Wattenhofer

For the regime-switching diffusion process with and without advection term we propose an integro-differential equation describing the densities of states continuously distributed over a segment. We demonstrate that there exists a…

Analysis of PDEs · Mathematics 2026-03-18 Alexander S. Bratus , Olga S. Rozanova

We analyse and interpret the effects of breaking detailed balance on the convergence to equilibrium of conservative interacting particle systems and their hydrodynamic scaling limits. For finite systems of interacting particles, we review…

Statistical Mechanics · Physics 2017-08-21 Marcus Kaiser , Robert L. Jack , Johannes Zimmer

We discuss a connection between a generative model, called the diffusion model, and nonequilibrium thermodynamics for the Fokker-Planck equation, called stochastic thermodynamics. Using techniques from stochastic thermodynamics, we derive…

Statistical Mechanics · Physics 2025-08-04 Kotaro Ikeda , Tomoya Uda , Daisuke Okanohara , Sosuke Ito

In this paper we focus on the development of new methods suitable for efficient and reliable coarse-graining of {\it non-equilibrium} molecular systems. In this context, we propose error estimation and controlled-fidelity model reduction…

Computational Physics · Physics 2015-06-15 Markos A. Katsoulakis , Petr Plechac