随机漫步和莱维飞行的最小值数量的普遍分布。

IF 2.2 3区 物理与天体物理 Q2 PHYSICS, FLUIDS & PLASMAS
Anupam Kundu, Satya N Majumdar, Grégory Schehr
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引用次数: 0

摘要

我们精确计算了由随机漫步或列维飞行产生的一维景观中局部极小值数量 m 的完整分布。我们考虑了两种不同的景观集合,一种是固定步数 N 的景观集合,另一种是直到随机漫步到达原点的第一次经过时间的景观集合。我们发现,m 的分布在两个集合中截然不同(前者为高斯分布,后者为幂律尾 m^{-3/2})。然而,我们的结果中最引人注目的一点是,在每种情况下,对于所有 m(而不仅仅是大 m),分布都是完全普遍的,即与随机游走中的跳跃分布无关。这意味着莱维飞行和具有有限跳跃方差的随机游走的分布完全相同。我们的分析结果与数值模拟结果非常吻合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Universal distribution of the number of minima for random walks and Lévy flights.

We compute exactly the full distribution of the number m of local minima in a one-dimensional landscape generated by a random walk or a Lévy flight. We consider two different ensembles of landscapes, one with a fixed number of steps N and the other till the first-passage time of the random walk to the origin. We show that the distribution of m is drastically different in the two ensembles (Gaussian in the former case, while having a power-law tail m^{-3/2} in the latter case). However, the most striking aspect of our results is that, in each case, the distribution is completely universal for all m (and not just for large m), i.e., independent of the jump distribution in the random walk. This means that the distributions are exactly identical for Lévy flights and random walks with finite jump variance. Our analytical results are in excellent agreement with our numerical simulations.

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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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