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Understanding the fluctuations by which phenomenological evolution equations with thermodynamic structure can be enhanced is the key to a general framework of nonequilibrium statistical mechanics. These fluctuations provide an idealized…

Statistical Mechanics · Physics 2021-02-03 Hans Christian Öttinger , Mark A. Peletier , Alberto Montefusco

For boundary-driven non-equilibrium Markov models of non-interacting particles in one dimension, either in continuous space with the Fokker-Planck dynamics involving an arbitrary force $F(x)$ and an arbitrary diffusion coefficient $D(x)$,…

Statistical Mechanics · Physics 2023-07-06 Cecile Monthus

We show that, on a $d-$dimensional hypercubic lattice with $d>1$, conserved-mass transport processes, with {\it multidirectional} hopping that respect all symmetries of the lattice, exhibit power-law correlations for generic parameter…

Statistical Mechanics · Physics 2025-10-27 Animesh Hazra , Tanmoy Chakraborty , Anirban Mukherjee , Punyabrata Pradhan

In the context of Markov evolution, we present two original approaches to obtain Generalized Fluctuation-Dissipation Theorems (GFDT), by using the language of stochastic derivatives and by using a family of exponential martingales…

Statistical Mechanics · Physics 2013-12-04 Raphael Chetrite , Shamik Gupta

In the general process of eliminating dynamic variables in Markovian models, there exists a difference in the irreversible entropy production between the original and reduced dynamics. We call this difference the hidden entropy production,…

Statistical Mechanics · Physics 2022-06-28 Kyogo Kawaguchi , Yohei Nakayama

A new class of Markov chain Monte Carlo (MCMC) algorithms, based on simulating piecewise deterministic Markov processes (PDMPs), have recently shown great promise: they are non-reversible, can mix better than standard MCMC algorithms, and…

Computation · Statistics 2020-10-23 Augustin Chevallier , Paul Fearnhead , Matthew Sutton

We discuss entropy production in nonequilibrium steady states by focusing on paths obtained by sampling at regular (small) intervals, instead of sampling on each change of the system's state. This allows us to study directly entropy…

Statistical Mechanics · Physics 2011-09-07 Daniel ben-Avraham , Sven Dorosz , Michel Pleimling

The purpose of this comment is to correct mistaken assumptions and claims made in the paper Stochastic feedback, nonlinear families of Markov processes, and nonlinear Fokker-Planck equations by T. D. Frank. Our comment centers on the claims…

Statistical Mechanics · Physics 2009-11-13 Joseph L. McCauley

Recently, a class of stochastic processes known as piecewise deterministic Markov processes has been used to define continuous-time Markov chain Monte Carlo algorithms with a number of attractive properties, including compatibility with…

Computation · Statistics 2019-06-03 Alexander Terenin , Daniel Thorngren

We develop a formalism to discuss the properties of GENERIC systems in terms of corresponding Hamiltonians that appear in the characterization of large-deviation limits. We demonstrate how the GENERIC structure naturally arises from a…

Analysis of PDEs · Mathematics 2020-03-31 Richard C. Kraaij , Alexandre Lazarescu , Christian Maes , Mark A. Peletier

For discrete-time stochastic processes, there is a close connection between return/waiting times and entropy. Such a connection cannot be straightforwardly extended to the continuous-time setting. Contrarily to the discrete-time case one…

Probability · Mathematics 2007-05-23 Jean-Rene Chazottes , Cristian Giardina , Frank Redig

The large deviations principle for the empirical measure for both continuous and discrete time Markov processes is well known. Various expressions are available for the rate function, but these expressions are usually as the solution to a…

Probability · Mathematics 2015-06-22 Paul Dupuis , Yufei Liu

We extend the hypocoercivity framework for piecewise-deterministic Markov process (PDMP) Monte Carlo established in [Andrieu et. al. (2018)] to heavy-tailed target distributions, which exhibit subgeometric rates of convergence to…

Probability · Mathematics 2021-06-03 Christophe Andrieu , Paul Dobson , Andi Q. Wang

The master equation and, more generally, Markov processes are routinely used as models for stochastic processes. They are often justified on the basis of randomization and coarse-graining assumptions. Here instead, we derive n-th order…

Statistical Mechanics · Physics 2012-09-27 Julian Lee , Steve Pressé

We aim at characterizing viability, invariance and some reachability properties of controlled piecewise deterministic Markov processes (PDMPs). Using analytical methods from the theory of viscosity solutions, we establish criteria for…

Optimization and Control · Mathematics 2013-04-09 D. Goreac

We investigate a method for extracting nonlinear principal components (NPCs). These NPCs maximize variation subject to smoothness and orthogonality constraints; but we allow for a general class of constraints and multivariate probability…

Methodology · Statistics 2009-08-06 Xioahong Chen , Lars Peter Hansen , Jose Scheinkman

The lack of formulation of macroscopic equations for irreversible dynamics of viscous heat-conducting media compatible with the causality principle of Einstein's Special Relativity and the Euler-Lagrange structure of General Relativity is a…

General Relativity and Quantum Cosmology · Physics 2020-03-31 Evgeniy Romenski , Ilya Peshkov , Michael Dumbser , and Francesco Fambri

A multiscale theory of interacting continuum mechanics and thermodynamics of mixtures of fluids, electrodynamics, polarization and magnetization is proposed. The mechanical (reversible) part of the theory is constructed in a purely…

Classical Physics · Physics 2020-08-26 Petr Vagner , Michal Pavelka , Ogul Esen

We investigate the steady-state entropy production rate (EPR) in the Hydrodynamic Vicsek Model (HVM) and Diffusive Flocking Model (DFM). Both models display a transition from an isotropic gas to a polar liquid (flocking) phase, in addition…

Statistical Mechanics · Physics 2020-12-21 Øyvind L. Borthne , Étienne Fodor , Michael E. Cates

We give a short overview of recent results on a specific class of Markov process: the Piecewise Deterministic Markov Processes (PDMPs). We first recall the definition of these processes and give some general results. On more specific cases…

Statistics Theory · Mathematics 2013-09-25 Romain Azaïs , Jean-Baptiste Bardet , Alexandre Genadot , Nathalie Krell , Pierre-André Zitt