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In this paper, we relate the coupling of Markov chains, at the basis of perfect sampling methods, with damage spreading, which captures the chaotic nature of stochastic dynamics. For two-dimensional spin glasses and hard spheres we point…

Statistical Mechanics · Physics 2022-09-02 Etienne P. Bernard , Cédric Chanal , Werner Krauth

Dimensional reduction is a generic consequence of dissipation in nonlinear evolution equations, often leading to attractor collapse and the loss of dynamical richness. To counteract this, we introduce a geometric framework for Covariant…

Chaotic Dynamics · Physics 2026-03-10 Pengyue Hou

In this paper we develop a probabilistic model of single-particle diffusion in 1D multi-layered media by constructing a multi-layered version of so-called snapping out Brownian motion (BM). The latter sews together successive rounds of…

Statistical Mechanics · Physics 2023-01-10 Paul C. Bressloff

Density dependent families of Markov chains, such as the stochastic models of mass-action chemical kinetics, converge for large values of the indexing parameter $N$ to deterministic systems of differential equations (Kurtz, 1970). Moreover…

Probability · Mathematics 2017-07-11 Enrico Bibbona , Roberta Sirovich

Recently, many interesting features of the hydrodynamically coupled motions of the Brownian particles in a viscous fluid have been reported which are impossible for the uncoupled motions of the similar particles. However, it is expected…

Soft Condensed Matter · Physics 2018-05-17 Shuvojit Paul

We consider a model of non-Markovian Quantum Brownian motion that consists of an harmonic oscillator bilinearly coupled to a thermal bath, both via its position and momentum operators. We derive the master equation for such a model and we…

Quantum Physics · Physics 2017-08-02 Luca Ferialdi , Andrea Smirne

We study a minimal non-Markovian model of superdiffusion which originates from long-range velocity correlations within the generalized Langevin equation (GLE) approach. The model allows for a three-dimensional Markovian embedding. The…

Statistical Mechanics · Physics 2015-05-19 P. Siegle , I. Goychuk , P. Hanggi

Dynamics of non-Markovian systems is a classic problem yet it attracts an everlasting activity in physics and beyond. A powerful tool for modeling such setups is the Generalized Langevin Equation, however, its analysis typically poses a…

Statistical Mechanics · Physics 2024-10-29 Mateusz Wiśniewski , Jakub Spiechowicz

While modern representation learning relies heavily on global error signals, decentralized algorithms driven by local interactions offer a fundamental distributed alternative. However, the macroscopic convergence properties of these…

Machine Learning · Computer Science 2026-04-21 Zilin Li , Weiwei Xu , Xuchun Tong , Xuanbo Lu , Xuanqi Zhao

We apply the macroscopic fluctuation theory (MFT) to study the large-scale dynamical properties of Brownian particles with arbitrary pairwise interaction. By combining it with standard results of equilibrium statistical mechanics for the…

Statistical Mechanics · Physics 2026-05-19 Aurélien Grabsch , Davide Venturelli , Olivier Bénichou

Oscillatory systems of interacting Hawkes processes with Erlang memory kernels were introduced in Ditlevsen (2017). They are piecewise deterministic Markov processes (PDMP) and can be approximated by a stochastic diffusion. First, a strong…

Numerical Analysis · Mathematics 2020-03-25 Julien Chevallier , Anna Melnykova , Irene Tubikanec

We analyze the microscopic model of quantum Brownian motion, describing a Brownian particle interacting with a bosonic bath through a coupling which is linear in the creation and annihilation operators of the bath, but may be a nonlinear…

Quantum Gases · Physics 2015-04-17 Pietro Massignan , Aniello Lampo , Jan Wehr , Maciej Lewenstein

Markov chain Monte Carlo (MCMC) is a powerful methodology for the approximation of posterior distributions. However, the iterative nature of MCMC does not naturally facilitate its use with modern highly parallel computation on HPC and cloud…

The need to calibrate increasingly complex statistical models requires a persistent effort for further advances on available, computationally intensive Monte Carlo methods. We study here an advanced version of familiar Markov Chain Monte…

Methodology · Statistics 2015-03-20 Alexandros Beskos , Konstantinos Kalogeropoulos , Erik Pazos

We use the system-plus-reservoir approach to study the dynamics of a system composed of two independent Brownian particles. We present an extension of the well-known model of a bath of oscillators which is capable of inducing an effective…

Statistical Mechanics · Physics 2009-11-11 O. S. Duarte , A. O. Caldeira

Recently, the task of image generation has attracted much attention. In particular, the recent empirical successes of the Markov Chain Monte Carlo (MCMC) technique of Langevin Dynamics have prompted a number of theoretical advances; despite…

Machine Learning · Statistics 2020-06-24 Adam Block , Youssef Mroueh , Alexander Rakhlin , Jerret Ross

This work gives sufficient conditions for uniqueness in law of semimartingale, obliquely reflecting Brownian motion in a nonpolyhedral, piecewise ${\cal C}^2$ cone, with radially constant, Lipschitz continuous direction of reflection on…

Probability · Mathematics 2025-01-27 Cristina Costantini

Active and diffusive motion in Brownian particles are regularly observed in fluidic environments, albeit at different time scales. Here, we experimentally study the dynamics of highly asymmetric microclusters trapped in air employing…

We study ergodic properties of a class of Markov-modulated general birth-death processes under fast regime switching. The first set of results concerns the ergodic properties of the properly scaled joint Markov process with a parameter that…

Probability · Mathematics 2019-09-17 Ari Arapostathis , Guodong Pang , Yi Zheng

We study the second-order asymptotics around the superdiffusive strong law~\cite{MMW} of a multidimensional driftless diffusion with oblique reflection from the boundary in a generalised parabolic domain. In the unbounded direction we prove…

Probability · Mathematics 2024-12-20 Aleksandar Mijatović , Isao Sauzedde , Andrew Wade