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The mean-squared displacement (MSD) is an averaged quantity widely used to assess anomalous diffusion. In many cases, such as molecular motors with finite processivity, dynamics of the system of interest produce trajectories of varying…

Statistical Mechanics · Physics 2020-10-07 Chapin S. Korosec , David A. Sivak , Nancy R. Forde

Within the framework of probability models for overdispersed count data, we propose the generalized fractional Poisson distribution (gfPd), which is a natural generalization of the fractional Poisson distribution (fPd), and the standard…

Probability · Mathematics 2021-01-12 Dexter Cahoy , Elvira Di Nardo , Federico Polito

Diffusion models generate samples by incrementally reversing a process that turns data into noise. We show that when the step size goes to zero, the reversed process is invariant to the distribution of these increments. This reveals a…

Machine Learning · Computer Science 2024-12-12 Henry Li

We demonstrate that continuous time random walks in which successive waiting times are correlated by Gaussian statistics lead to anomalous diffusion with mean squared displacement <r^2(t)>~t^{2/3}. Long-ranged correlations of the waiting…

Statistical Mechanics · Physics 2015-05-14 Vincent Tejedor , Ralf Metzler

We analyze numerically a forward-backward diffusion equation with a cubic-like diffusion function, -emerging in the framework of phase transitions modeling- and its "entropy" formulation determined by considering it as the singular limit of…

Analysis of PDEs · Mathematics 2010-08-31 Pauline Lafitte , Corrado Mascia

We present a new theoretical framework for Diffusion Limited Aggregation and associated Dielectric Breakdown Models in two dimensions. Key steps are understanding how these models interrelate when the ultra-violet cut-off strategy is…

Statistical Mechanics · Physics 2007-05-23 R. C. Ball , E. Somfai

Classical diffusion in a random medium involves an exponential functional of Brownian motion. This functional also appears in the study of Brownian diffusion on a Riemann surface of constant negative curvature. We analyse in detail this…

Condensed Matter · Physics 2016-08-31 Alain COMTET , Cecile MONTHUS

We construct a new random probability measure on the sphere and on the unit interval which in both cases has a Gibbs structure with the relative entropy functional as Hamiltonian. It satisfies a quasi-invariance formula with respect to the…

Probability · Mathematics 2007-05-23 Max-K von Renesse , Karl-Theodor Sturm

The various types of generalized Cattaneo, called also telegrapher's equation, are studied. We find conditions under which solutions of the equations considered so far can be recognized as probability distributions, \textit{i.e.} are…

Statistical Mechanics · Physics 2020-08-19 K. Górska , A. Horzela , E. K. Lenzi , G. Pagnini , T. Sandev

The purpose of this tutorial is to introduce the main concepts behind normal and anomalous diffusion. Starting from simple, but well known experiments, a series of mathematical modeling tools are introduced, and the relation between them is…

Chaotic Dynamics · Physics 2008-05-06 Loukas Vlahos , Heinz Isliker , Yannis Kominis , Kyriakos Hizanidis

Diffusion probabilistic models excel at sampling new images from learned distributions. Originally motivated by drift-diffusion concepts from physics, they apply image perturbations such as noise and blur in a forward process that results…

Image and Video Processing · Electrical Eng. & Systems 2024-06-07 Pascal Peter

We study the diffusion of an ensemble of overdamped particles sliding over a tilted random poten- tial (produced by the interaction of a particle with a random polymer) with long-range correlations. We found that the diffusion properties of…

Disordered Systems and Neural Networks · Physics 2014-04-11 R. Salgado-Garcia , Cesar Maldonado

We present a Master Equation formulation based on a Markovian random walk model that exhibits sub-diffusion, classical diffusion and super-diffusion as a function of a single parameter. The non-classical diffusive behavior is generated by…

Statistical Mechanics · Physics 2013-09-19 James F. Lutsko , Jean Pierre Boon

In this work we establish a link between two different phenomena that were studied in a large and growing number of biological, composite and soft media: the diffusion in compartmentalized environment and the Brownian yet non-Gaussian…

Statistical Mechanics · Physics 2020-08-05 Jakub Ślęzak , Stanislav Burov

The diffusion of hard-core particles subject to a global bias is described by a nonlinear, anisotropic generalization of the diffusion equation with conserved, local noise. Using renormalization group techniques, we analyze the effect of an…

Statistical Mechanics · Physics 2015-06-25 H. K. Janssen , B. Schmittmann

In recent years, several experiments highlighted a new type of diffusion anomaly, which was called Brownian yet non-Gaussian diffusion. In systems displaying this behavior, the mean squared displacement of the diffusing particles grows…

Statistical Mechanics · Physics 2023-08-01 Adrian Pacheco-Pozo , Igor M. Sokolov

We study asymptotic properties of diffusion and other transport processes (including self-avoiding walks and electrical conduction) on large randomly branched polymers using renormalized dynamical field theory. We focus on the swollen phase…

Statistical Mechanics · Physics 2015-06-04 Hans-Karl Janssen , Olaf Stenull

Expanding medium is very common in many different fields, such as biology and cosmology. It brings a nonnegligible influence on particle's diffusion, which is quite different from the effect of an external force field. The dynamic mechanism…

Statistical Mechanics · Physics 2023-02-22 Xudong Wang , Yao Chen

One key issue in the probability density function (PDF) approach for disperse two-phase turbulent flows is to close the diffusion term in the phase space. This study aimed to derive a kinetic equation for particle dispersion in turbulent…

Statistical Mechanics · Physics 2020-07-15 De-yu Zhong , Guang-qian Wang , Tie-jian Li , Ming-xi Zhang , You Xia

In network embedding, random walks play a fundamental role in preserving network structures. However, random walk based embedding methods have two limitations. First, random walk methods are fragile when the sampling frequency or the number…

Machine Learning · Computer Science 2018-05-14 Yong Shi , Minglong Lei , Peng Zhang , Lingfeng Niu
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