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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

In this work we study the transition from normal to anomalous diffusion of Brownian particles on disordered potentials. The potential model consists of a series of "potential hills" (defined on unit cell of constant length) whose heights…

Disordered Systems and Neural Networks · Physics 2016-03-23 R. Salgado-Garcia

We show analytically that there is anomalous diffusion when the diffusion constant depends on the concentration as a power law with a positive exponent or a negative exponent with absolute value less than one and the initial condition is a…

Statistical Mechanics · Physics 2019-12-13 Alex Hansen , Eirik G. Flekkøy

Particles driven through a periodic potential by an external constant force are known to exhibit a pronounced peak of the diffusion around a critical force that defines the transition between locked and running states. It has recently been…

Soft Condensed Matter · Physics 2015-05-19 M. Khoury , A. M. Lacasta , J. M. Sancho , Katja Lindenberg

Anomalous-diffusion, the departure of the spreading dynamics of diffusing particles from the traditional law of Brownian-motion, is a signature feature of a large number of complex soft-matter and biological systems. Anomalous-diffusion…

The diffusion of a particle in a crowded environment typically proceeds through three regimes: for very short times the particle diffuses freely until it collides with an obstacle for the first time, while for very long times diffusion the…

Biological Physics · Physics 2019-10-09 Nguiya P. Neo , Gary W. Slater

Diffusive dynamics abound in nature and have been especially studied in physical, biological, and financial systems. These dynamics are characterised by a linear growth of the mean squared displacement (MSD) with time. Often, the conditions…

Statistical Mechanics · Physics 2025-11-14 Alvaro Lanza , Xiang Qu , Stefano Bo

Diffusive transport of a particle in spatially correlated random energy landscape having exponential density of states has been considered. We exactly calculate the diffusivity in the nondispersive quasi-equilibrium transport regime and…

Disordered Systems and Neural Networks · Physics 2018-02-14 S. V. Novikov

We present analytical results for the biased diffusion of particles moving under a constant force in a randomly layered medium. The influence of this medium on the particle dynamics is modeled by a piecewise constant random force. The…

Statistical Mechanics · Physics 2010-02-10 S. I. Denisov , H. Kantz

We show that anomalous diffusion arises in two different models for the motion of randomly forced and weakly damped particles: one is a generalisation of the Ornstein-Uhlenbeck process with a random force which depends on position as well…

Chaotic Dynamics · Physics 2013-03-05 Vlad Bezuglyy , Michael Wilkinson , Bernhard Mehlig

In this work we study analytically and numerically the transport properties of non-interacting active particles moving on a $d$-dimensional disordered media. The disorder in the space is modeled by means of a set of non-overlapping…

Statistical Mechanics · Physics 2022-07-20 R. Salgado-García

Infiltration of diffusing particles from one material to another where the diffusion mechanism is either normal or anomalous is a widely observed phenomena. When the diffusion is anomalous we find interesting behaviors: diffusion may lead…

Statistical Mechanics · Physics 2010-05-04 Nickolay Korabel , Eli Barkai

We propose a model of sub-diffusion in which an external force is acting on a particle at all times not only at the moment of jump. The implication of this assumption is the dependence of the random trapping time on the force with the…

Statistical Mechanics · Physics 2015-04-16 Sergei Fedotov , Nickolay Korabel

We study diffusion of a particle in a system composed of K parallel channels, where the transition rates within the channels are quenched random variables whereas the inter-channel transition rate v is homogeneous. A variant of the strong…

Disordered Systems and Neural Networks · Physics 2015-05-14 R. Juhász , F. Iglói

We examine characteristic properties of deterministic and stochastic diffusion in low-dimensional chaotic dynamical systems. As an example, we consider a periodic array of scatterers defined by a simple chaotic map on the line. Adding…

Chaotic Dynamics · Physics 2009-11-07 R. Klages

Anomalous diffusion occurs at very different scales in nature, from atomic systems to motions in cell organelles, biological tissues or ecology, and also in artificial materials, such as cement. Being able to accurately measure the…

Machine Learning · Computer Science 2021-08-09 Òscar Garibo i Orts , Miguel A. Garcia-March , J. Alberto Conejero

The overdamped dynamics of a charged particle driven by an uniform electric field through a random sequence of scatterers in one dimension is investigated. Analytic expressions of the mean velocity and of the velocity power spectrum are…

Chaotic Dynamics · Physics 2009-11-07 H. Kunz , R. Livi , A. Suto

Diffusion is a fundamental physical phenomenon with critical applications in fields such as metallurgy, cell biology, and population dynamics. While standard diffusion is well-understood, anomalous diffusion often requires complex non-local…

Statistical Mechanics · Physics 2026-01-16 Gabriel Barreiro , Vladimir Pérez-Veloz

We study the Brownian motion of a classical particle in one-dimensional inhomogeneous environments where the transition probabilities follow quasiperiodic or aperiodic distributions. Exploiting an exact correspondence with the…

Statistical Mechanics · Physics 2009-10-31 F. Igloi , L. Turban , H. Rieger

Anomalous diffusion occurs in many physical and biological phenomena, when the growth of the mean squared displacement (MSD) with time has an exponent different from one. We show that recurrent neural networks (RNN) can efficiently…

Statistical Mechanics · Physics 2019-07-24 Stefano Bo , Falko Schmidt , Ralf Eichhorn , Giovanni Volpe
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