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Multi-agent systems can be successfully described by kinetic models, which allow one to explore the large scale aggregate trends resulting from elementary microscopic interactions. The latter may be formalised as collision-like rules, in…

Statistical Mechanics · Physics 2020-11-06 Nadia Loy , Andrea Tosin

Jump Markov linear models consists of a finite number of linear state space models and a discrete variable encoding the jumps (or switches) between the different linear models. Identifying jump Markov linear models makes for a challenging…

Computation · Statistics 2015-02-17 Andreas Svensson , Thomas B. Schön , Fredrik Lindsten

The Green-Kubo formula relates the spatial diffusion coefficient to the stationary velocity autocorrelation function. We derive a generalization of the Green-Kubo formula valid for systems with long-range or nonstationary correlations for…

Statistical Mechanics · Physics 2014-03-05 A. Dechant , E. Lutz , D. A. Kessler , E. Barkai

This paper establishes limit theorems for a class of stochastic hybrid systems (continuous deterministic dynamic coupled with jump Markov processes) in the fluid limit (small jumps at high frequency), thus extending known results for jump…

Probability · Mathematics 2010-01-15 K. Pakdaman , M. Thieullen , G. Wainrib

We consider a nonlinear stochastic differential equation driven by an $\alpha$-stable L\'{e}vy process ($1<\alpha<2$). We first obtain some regularity results for the probability density of its invariant measure via establishing the a…

Probability · Mathematics 2020-08-17 Qi Zhang , Jinqiao Duan

In real-world geophysical applications (such as predicting the climate change), the reduced models of real-world complex multiscale dynamics are used to predict the response of the actual multiscale climate to changes in various global…

Dynamical Systems · Mathematics 2013-07-30 Rafail V. Abramov , Marc P. Kjerland

The classical theory of linear response applies to statistical mechanics close to equilibrium. Away from equilibrium, one may describe the microscopic time evolution by a general differentiable dynamical system, identify nonequilibrium…

Chaotic Dynamics · Physics 2009-11-13 David Ruelle

We present an extension to Kolmogorov's refined similarity hypothesis for universal fully developed turbulence. The extension is applied within Z. She and E. Leveque's multifractal model of inertial range scaling and its generalizations.…

Fluid Dynamics · Physics 2022-09-28 Jarret Petrillo , James Glimm

Diffusion models have emerged as a powerful framework in generative modeling, typically relying on optimizing neural networks to estimate the score function via forward SDE simulations. In this work, we propose an alternative method that is…

Numerical Analysis · Mathematics 2025-06-17 Yuehaw Khoo , Mathias Oster , Yifan Peng

We consider the asymptotic behaviour of the fluctuation process for large stochastic systems of interacting particles driven by both idiosyncratic and common noise with an interaction kernel \(k \in L^2(\R^d) \cap L^\infty(\R^d)\). Our…

Probability · Mathematics 2026-05-28 Paul Nikolaev

Reaction-diffusion equations deliver a versatile tool for the description of reactions in inhomogeneous systems under the assumption that the characteristic reaction scales and the scales of the inhomogeneities in the reactant…

Statistical Mechanics · Physics 2009-11-11 M. G. W. Schmidt , F. Sagues , I. M. Sokolov

Discrete diffusion models have gained increasing attention for their ability to model complex distributions with tractable sampling and inference. However, the error analysis for discrete diffusion models remains less well-understood. In…

Machine Learning · Computer Science 2025-03-04 Yinuo Ren , Haoxuan Chen , Grant M. Rotskoff , Lexing Ying

Although diffusion models now occupy a central place in generative modeling, introductory treatments commonly assume Euclidean data and seldom clarify their connection to discrete-state analogues. This article is a self-contained primer on…

Machine Learning · Statistics 2025-12-05 Vincent Pauline , Tobias Höppe , Kirill Neklyudov , Alexander Tong , Stefan Bauer , Andrea Dittadi

The use of reaction-diffusion models rests on the key assumption that the underlying diffusive process is Gaussian. However, a growing number of studies have pointed out the prevalence of anomalous diffusion, and there is a need to…

Pattern Formation and Solitons · Physics 2009-11-07 D. del-Castillo-Negrete , B. A. Carreras , V. E. Lynch

Long-range interacting N-particle systems get trapped into long-living out-of-equilibrium stationary states called quasi-stationary states (QSS). We study here the response to a small external perturbation when such systems are settled into…

Statistical Mechanics · Physics 2014-11-20 Aurelio Patelli , Stefano Ruffo

A new asymptotic method is presented for the analysis of the traveling waves in the one-dimensional reaction-diffusion system with the diffusion with a finite velocity and Kolmogorov-Petrovskii-Piskunov kinetics. The analysis makes use of…

Statistical Mechanics · Physics 2007-05-23 Sergei Fedotov

The sensitivity of climate models to increasing CO2 concentration and the climate response at decadal time scales are still major factors of uncertainty for the assessment of the long and short term effects of anthropogenic climate change.…

Atmospheric and Oceanic Physics · Physics 2016-03-23 Francesco Ragone , Valerio Lucarini , Frank Lunkeit

In this paper, we develop a general law of large numbers and central limit theorem for cumulative reward processes associated with finite state Markov jump processes with non-stationary transition rates. Such models commonly arise in…

Probability · Mathematics 2025-10-15 Monte Fischer , Peter W. Glynn

A collisional model of a confined quasi-two-dimensional granular mixture is considered to analyze homogeneous steady states. The model includes an effective mechanism to transfer the kinetic energy injected by vibration in the vertical…

Statistical Mechanics · Physics 2020-12-30 Ricardo Brito , Rodrigo Soto , Vicente Garzó

The recently developed short-time linear response algorithm, which predicts the average response of a nonlinear chaotic system with forcing and dissipation to small external perturbation, generally yields high precision of the response…

Dynamical Systems · Mathematics 2016-04-08 Rafail V. Abramov