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The real-space renormalization group technique is introduced to evaluate the effective diffusion constant for diffusion in inhomogeneous media, which has been obtained by singular perturbation methods. Our method is formulated on a…

Disordered Systems and Neural Networks · Physics 2007-05-23 Mitsuhiro Kawasaki

We study the problem of a Brownian particle diffusing in finite dimensions in a potential given by $\psi= \phi^2/2$ where $\phi$ is Gaussian random field. Exact results for the diffusion constant in the high temperature phase are given in…

Disordered Systems and Neural Networks · Physics 2009-11-11 C. Touya , D. S. Dean

The behavior of the self diffusion constant of Langevin particles interacting via a pairwise interaction is considered. The diffusion constant is calculated approximately within a perturbation theory in the potential strength about the bare…

Soft Condensed Matter · Physics 2009-11-10 D. S. Dean , A. Lefèvre

The effective diffusion of Brownian particles in periodic potential has been a central topic in nonequilibrium statistical physcis. A classical result is the Lifson formula which provides the effective diffusion constant in periodic…

Statistical Mechanics · Physics 2026-01-22 Sang Yang , Zhixin Peng

We examine the dispersion of Brownian particles in a symmetric two dimensional channel, this classical problem has been widely studied in the literature using the so called Fick-Jacobs' approximation and its various improvements. Most…

Statistical Mechanics · Physics 2019-05-29 M. Mangeat , T. Guérin , D. S. Dean

The scaling form of the whole distribution P(D) of the random diffusion coefficient D(x) in a model of classically diffusing particles is investigated. The renormalization group approach above the lower critical dimension d=0 is applied to…

Condensed Matter · Physics 2009-10-22 Yan-Chr Tsai , Yonathan Shapir

The exact renormalization group is applied to a nonlinear diffusion equation with a discontinuous diffusion coefficient. The generating functional of the solution for the initial-value problem of nonlinear diffusion equations is first…

Statistical Mechanics · Physics 2009-11-11 S. Yoshida , T. Fukui

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

The paper is an attempt to relate two vast areas of the applicability of the renormalization group (RG): field theoretic models and partial differential equations. It is shown that the Green function of a nonlinear diffusion equation can be…

Chaotic Dynamics · Physics 2008-12-18 N. V. Antonov , Juha Honkonen

The one-dimensional overdamped Brownian motion in a symmetric periodic potential modulated by external time-reversible noise is analyzed. The calculation of the effective diffusion coefficient is reduced to the mean first passage time…

Statistical Mechanics · Physics 2009-11-11 Bernardo Spagnolo , Alexander Dubkov

We develop the exact renormalization group approach as a way to evaluate the effective speed of propagation of a scalar wave in a medium with random inhomogeneities. We use the Martin-Siggia-Rose formalism to translate the problem into a…

Disordered Systems and Neural Networks · Physics 2017-08-02 Federico Lamagna , Esteban Calzetta

A Brownian particle floating in a narrow corrugated (sinusoidal) channel with fluctuating cross section exhibits non-Gaussian normal diffusion. Its displacements are distributed according to a Gaussian law for very short and asymptotically…

Statistical Mechanics · Physics 2019-10-09 Yunyun Li , Fabio Marchesoni , Debajyoti Debnath , Pulak K. Ghosh

We develop perturbation theory and physically motivated resummations of the perturbation theory for the problem of a tracer particle diffusing in a random media. The random media contains point scatterers of density $\rho$ uniformly…

Disordered Systems and Neural Networks · Physics 2009-11-10 D. S. Dean , I. T. Drummond , R. R. Horgan , A. Lefevre

In this paper we present an efficient numerical approach based on the Renormalization Group method for the computation of self-similar dynamics. The latter arise, for instance, as the long-time asymptotic behavior of solutions to nonlinear…

Analysis of PDEs · Mathematics 2016-09-06 Gastao A. Braga , Frederico Furtado , Jussara M. Moreira , Leonardo T. Rolla

We investigate the relationship between the effective diffusivity and effective drift of a particle moving in a random medium. The velocity of the particle combines a white noise diffusion process with a local drift term that depends…

Condensed Matter · Physics 2009-10-28 D. S. Dean , I. T. Drummond , R. R. Horgan

We investigate the Brownian diffusion of particles in one spatial dimension and in the presence of finite regions within which particles can either evaporate or be reset to a given location. For open boundary conditions, we highlight the…

Statistical Mechanics · Physics 2020-11-04 Gennaro Tucci , Andrea Gambassi , Shamik Gupta , Édgar Roldán

Extending the usual Ginzburg-Landau theory for the random-field Ising model, the possibility of dimensional reduction is reconsidered. A renormalization group for the probability distribution of magnetic impurities is applied. New…

Statistical Mechanics · Physics 2014-04-15 Hisamitsu Mukaida , Yoshinori Sakamoto

We obtain non-asymptotic Gaussian concentration bounds for the difference between the invariant measure $\nu$ of an ergodic Brownian diffusion process and the empirical distribution of an approximating scheme with decreasing time step along…

Probability · Mathematics 2018-05-28 Igor Honoré , Stephane Menozzi , Gilles Pagès

For an ergodic Brownian diffusion with invariant measure $\nu$, we consider a sequence of empirical distributions ($\nu$n) n$\ge$1 associated with an approximation scheme with decreasing time step ($\gamma$n) n$\ge$1 along an adapted…

Probability · Mathematics 2018-10-09 I Honoré

Diffusion models represent a class of generative models that produce data by denoising a sample corrupted by white noise. Despite the success of diffusion models in computer vision, audio synthesis, and point cloud generation, so far they…

Statistical Mechanics · Physics 2025-01-17 Kanta Masuki , Yuto Ashida
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