具有空间周期性噪声的随机过程的有效扩散常数。

IF 2.2 3区 物理与天体物理 Q2 PHYSICS, FLUIDS & PLASMAS
Stefano Giordano, Ralf Blossey
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引用次数: 0

摘要

我们讨论了具有空间相关噪声的随机过程的有效扩散常数 D_{eff}。从朗格文方程给出的随机过程出发,根据离散化规则 0≤α≤1 的选择,可以推导出不同的漂移-扩散方程。我们首先研究了无漂移的周期性异质扩散情况,并确定了有效扩散系数 D_{eff} 的一般结果,该结果对任何 α 值都有效。我们详细研究了周期性正弦扩散的情况,并找到了与 Legendre 函数的关系。然后,我们从 Lifson-Jackson 定理出发,推导出具有周期性空间噪声的扩散和存在漂移项的一般 α 的 D_{eff}。我们通过对漂移项和扩散项的一般周期性选择进行分析和数值计算来说明我们的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effective diffusion constant of stochastic processes with spatially periodic noise.

We discuss the effective diffusion constant D_{eff} for stochastic processes with spatially dependent noise. Starting from a stochastic process given by a Langevin equation, different drift-diffusion equations can be derived depending on the choice of the discretization rule 0≤α≤1. We initially study the case of periodic heterogeneous diffusion without drift, and we determine a general result for the effective diffusion coefficient D_{eff}, which is valid for any value of α. We study the case of periodic sinusoidal diffusion in detail, and we find a relationship with Legendre functions. Then we derive D_{eff} for general α in the case of diffusion with periodic spatial noise and in the presence of a drift term, generalizing the Lifson-Jackson theorem. Our results are illustrated by analytical and numerical calculations on generic periodic choices for drift and diffusion terms.

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来源期刊
Physical Review E
Physical Review E PHYSICS, FLUIDS & PLASMASPHYSICS, MATHEMAT-PHYSICS, MATHEMATICAL
CiteScore
4.50
自引率
16.70%
发文量
2110
期刊介绍: Physical Review E (PRE), broad and interdisciplinary in scope, focuses on collective phenomena of many-body systems, with statistical physics and nonlinear dynamics as the central themes of the journal. Physical Review E publishes recent developments in biological and soft matter physics including granular materials, colloids, complex fluids, liquid crystals, and polymers. The journal covers fluid dynamics and plasma physics and includes sections on computational and interdisciplinary physics, for example, complex networks.
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