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We construct a two-class asymmetric interacting particle system with $U_q(so_6)$ or $U_q(so_8)$ symmetry, in which up to two particles may occupy a site if the two particles have different class. The particles exhibit a drift, but there is…

Probability · Mathematics 2020-11-30 Jeffrey Kuan , Mark Landry , Andrew Lin , Andrew Park , Zhengye Zhou

Markov-modulated Brownian motion is a popular tool to model continuous-time phenomena in a stochastic context. The main quantity of interest is the invariant density, which satisfies a differential equation associated with the quadratic…

Probability · Mathematics 2016-05-06 Giang T. Nguyen , Federico Poloni

Interacting particle systems are continuous time Markov processes which are used to construct models in many disciplines. Monotonicity is a property that some interacting particle systems possess. A monotone interacting particle system is…

Probability · Mathematics 2010-11-11 Joseph Stover

Most of the published articles on random motions have been devoted to the study of the telegraph process or its generalizations that describe the random motion in $R^n$ of a single particle in a Markov or semi-Markov medium. However, up to…

Probability · Mathematics 2013-12-30 A. A. Pogorui

A Quark-Meson Coupling (QMC) model is extended to finite nuclei in the relativistic mean-field or Hartree approximation. The ultra-relativistic quarks are assumed to be bound in non-overlapping nucleon bags, and the interaction between…

Nuclear Theory · Physics 2008-11-26 Peter G. Blunden , Gerald A. Miller

The parameters of a discrete stationary Markov model are transition probabilities between states. Traditionally, data consist in sequences of observed states for a given number of individuals over the whole observation period. In such a…

Computation · Statistics 2012-04-30 Alberto Pasanisi , Shuai Fu , Nicolas Bousquet

In this paper, we introduce Max Markov Chain (MMC), a novel representation for a useful subset of High-order Markov Chains (HMCs) with sparse correlations among the states. MMC is parsimony while retaining the expressiveness of HMCs. Even…

Artificial Intelligence · Computer Science 2022-11-04 Yu Zhang , Mitchell Bucklew

Understanding and predicting how complex systems respond to external perturbations is a central challenge in nonequilibrium statistical physics. Here we consider continuous-time Markov networks, which we subject to perturbations along a…

Statistical Mechanics · Physics 2026-02-25 Robin Bebon , Thomas Speck

The notion of a successful coupling of Markov processes, based on the idea that both components of the coupled system ``intersect'' in finite time with probability one, is extended to cover situations when the coupling is unnecessarily…

Probability · Mathematics 2007-05-23 Michael Blank , Sergey Pirogov

We provide a numerical study of the macroscopic model of [3] derived from an agent-based model for a system of particles interacting through a dynamical network of links. Assuming that the network remodelling process is very fast, the…

Determining, understanding, and predicting the so-called structure-property relation is an important task in many scientific disciplines, such as chemistry, biology, meteorology, physics, engineering, and materials science. Structure refers…

Machine Learning · Computer Science 2023-11-15 Binh Duong Nguyen , Pavlo Potapenko , Aytekin Dermici , Kishan Govind , Sébastien Bompas , Stefan Sandfeld

In systems of programmable matter, we are given a collection of simple computation elements (or particles) with limited (constant-size) memory. We are interested in when they can self-organize to solve system-wide problems of movement,…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-02-27 Sarah Cannon , Joshua J. Daymude , Dana Randall , Andréa W. Richa

Markov Chain Monte Carlo (MCMC) methods are algorithms for sampling probability distributions, commonly applied to the Boltzmann distribution in physical and chemical models such as protein folding and the Ising model. These methods enable…

Quantum Physics · Physics 2025-12-04 Aingeru Ramos , Jose A. Pascual , Javier Navaridas , Ivan Coluzza

The main subject of the thesis is the study of stationary nonequilibrium states trough the use of microscopic stochastic models that encode the physical interaction in the rules of Markovian dynamics for particles configurations. These…

Statistical Mechanics · Physics 2023-02-07 Leonardo De Carlo

We investigate multivariate regular variation in the context of time-homogeneous Markov chains on general vector spaces and in random coefficient linear models. In the first part, we show that the regular variation of the stationary…

Probability · Mathematics 2025-10-23 Piotr Dyszewski , Tamara Mika

We summarize recent work showing that the $1/r^2$ model of interacting particles in 1-dimension is a universal Hamiltonian for quantum chaotic systems. The problem is analyzed in terms of random matrices and of the evolution of their…

Condensed Matter · Physics 2025-07-04 B. Sriram Shastry

We study a quantum theory based on two assumptions: In the intrinsic frame of reference of an isolated, macroscopic system, (i) the system has no global motion and is not entangled with any other system, (ii) time evolution of statevectors…

Quantum Physics · Physics 2016-09-08 Wen-ge Wang

We study a system of $N$ interacting particles on $\bf{Z}$. The stochastic dynamics consists of two components: a free motion of each particle (independent random walks) and a pair-wise interaction between particles. The interaction belongs…

Probability · Mathematics 2011-10-25 A. Manita , V. Shcherbakov

Computer simulations, especially of complex phenomena, can be expensive, requiring high-performance computing resources. Often, to understand a phenomenon, multiple simulations are run, each with a different set of simulation input…

Machine Learning · Computer Science 2023-07-06 Chandrika Kamath , Juliette S. Franzman , Brian H. Daub

Dense strongly interacting matter can exhibit regimes with spatial modulations, akin to crystalline phases. In this case particles can have a moat spectrum with minimal energy at nonzero momentum. We show that particle interferometry is a…

High Energy Physics - Phenomenology · Physics 2023-06-21 Fabian Rennecke , Robert D. Pisarski , Dirk H. Rischke