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We study the thermal Markovian diffusion of tracer particles in a 2D medium with spatially-varying diffusivity $D(r)$, mimicking recently measured, heterogeneous maps of the apparent diffusion coefficient in biological cells. For this…

Statistical Mechanics · Physics 2014-12-24 Andrey G. Cherstvy , Aleksei V. Chechkin , Ralf Metzler

Conventional approaches for simulating steady-state distributions of particles under diffusive and advective transport at high P\'eclet numbers involve solving the diffusion and advection equations in at least two dimensions. Here, we…

In this paper we consider parameter estimation for discretely observed diffusion processes. In particular, we focus on data that are observed at low frequency and methodology that can estimate parameters with uncertainty quantification.…

Computation · Statistics 2026-05-01 Jingning Yao , Ajay Jasra , Sheng Jiang

The goal of the paper is to describe the large time behaviour of a Markov process associated with a symmetric diffusion in a high-contrast random environment and to characterize the limit semigroup and the limit process under the diffusive…

Probability · Mathematics 2021-07-13 Brahim Amaziane , Andrey Piatnitski , Elena Zhizhina

Monte Carlo (MC) simulations of transport in random porous networks indicate that for high variances of the log-normal permeability distribution, the transport of a passive tracer is non-Fickian. Here we model this non-Fickian dispersion in…

Computational Physics · Physics 2018-08-01 Amir H. Delgoshaie , Patrick Jenny , Hamdi A. Tchelepi

A general method is proposed which allows one to estimate drift and diffusion coefficients of a stochastic process governed by a Langevin equation. It extends a previously devised approach [R. Friedrich et al., Physics Letters A 271, 217…

Data Analysis, Statistics and Probability · Physics 2009-11-11 D. Kleinhans , R. Friedrich , A. Nawroth , J. Peinke

In this paper Gaussian models of retarded and accelerated anomalous diffusion are considered. Stochastic differential equations of fractional order driven by single or multiple fractional Gaussian noise terms are introduced to describe…

Statistical Mechanics · Physics 2014-05-08 Chai Hok Eab , S. C. Lim

We are studying stationary random processes with conditional polynomial moments that allow a continuous path modification. Processes with continuous path modification, are important because they are relatively easy to simulate. One does not…

Probability · Mathematics 2024-11-21 Paweł J. Szabłowski

This paper uses the generator approach of Stein's method to analyze the gap between steady-state distributions of Markov chains and diffusion processes. Until now, the standard way to invoke Stein's method for this problem was to use the…

Probability · Mathematics 2022-02-15 Anton Braverman

A discrete-time stochastic process derived from a model of basketball is used to generalize any discrete distribution. The generalized distributions can have one or two more parameters than the parent distribution. Those derived from…

Applications · Statistics 2020-06-25 Rose Baker

Multiple time scales problems are investigated by combining geometrical and analytical approaches. More precisely, for fast-slow reaction-diffusion systems, we first prove the existence of slow manifolds for the abstract problem under the…

Analysis of PDEs · Mathematics 2025-01-29 Laurent Desvillettes , Christian Kuehn , Jan-Eric Sulzbach , Bao Quoc Tang , Bao-Ngoc Tran

The goal of this paper is to find the homogenized equation of a heterogenous Fisher-KPP model in a periodic medium. The solutions of this model are pulsating travelling fronts whose \emph{speeds} are superior to a parametric minimal speed…

Analysis of PDEs · Mathematics 2012-02-01 Mohammad El Smaily

We investigate the role of the form of the spatial diffusion coefficient in shock acceleration of fast particles. Referring to non-classical diffusion and using the results of numerical (hybrid) simulations tailored for the downstream shock…

Astrophysics · Physics 2008-11-25 R. A. Treumann

We consider the motion of a particle in a periodic two dimensional flow perturbed by small (molecular) diffusion. The flow is generated by a divergence free zero mean vector field. The long time behavior corresponds to the behavior of the…

Probability · Mathematics 2009-03-04 L. Koralov

We study the one-dimensional diffusion process which takes place between two reflecting boundaries and which is acted upon by a time-dependent and spatially-constant force. The assumed force possesses both the harmonically oscillating and…

Other Condensed Matter · Physics 2007-05-23 Evzen Subrt , Petr Chvosta

Recently, a new formalism describing the anomalous diffusion processes, based on the Onsager-Machlup fluctuation theory, has been suggested \cite{Smain, Spub}. We study particles performing this new type of motion, under the action of…

Statistical Mechanics · Physics 2025-08-26 A. S. Bodrova , S. I. Serdyukov

Diffusion models are loosely modelled based on non-equilibrium thermodynamics, where \textit{diffusion} refers to particles flowing from high-concentration regions towards low-concentration regions. In statistics, the meaning is quite…

Machine Learning · Computer Science 2023-12-19 Inga Strümke , Helge Langseth

We consider a Markovian jumping process which is defined in terms of the jump-size distribution and the waiting-time distribution with a position-dependent frequency, in the diffusion limit. We assume the power-law form for the frequency.…

Statistical Mechanics · Physics 2015-07-20 T. Srokowski , A. Kaminska

In this paper we study the Hamiltonian dynamics of charged particles subject to a non-self-consistent stochastic electric field, when the plasma is in the so-called weak turbulent regime. We show that the asymptotic limit of the Vlasov…

Analysis of PDEs · Mathematics 2021-10-13 Claude Bardos , Nicolas Besse

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