具有受控分支活动的随机树的阿米巴蒙特卡洛算法：高效的试验移动生成和通用动力学。

IF 2.2 3区物理与天体物理 Q2 PHYSICS, FLUIDS & PLASMAS

Physical Review E Pub Date : 2024-10-01 DOI:10.1103/PhysRevE.110.045312

Pieter H W van der Hoek, Angelo Rosa, Ralf Everaers

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引用次数: 0

摘要

爬行蒙特卡洛算法是一种简单、基于物理原理的高效方法，用于平衡线性聚合物的半稀释溶液。在这里，我们提出了两个简单的概括，用于随机分支链的类似阿米巴算法，使我们能够有效地处理具有可控分支活动的随机树。我们分析了阿米巴算法丰富的松弛动力学，并证明了树松弛存在意想不到的缩放机制。我们的结果表明，阿米巴算法的平衡时间一般按 N^{2}〈n_{lin}〉^{Δ}的方式缩放，其中 N 表示树节点的数量，〈n_{lin}〉表示树由线性段组成的平均数量，Δ≃0.4。

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Amoeba Monte Carlo algorithms for random trees with controlled branching activity: Efficient trial move generation and universal dynamics.

The reptation Monte Carlo algorithm is a simple, physically motivated and efficient method for equilibrating semidilute solutions of linear polymers. Here, we propose two simple generalizations for the analog Amoeba algorithm for randomly branching chains, which allow us to efficiently deal with random trees with controlled branching activity. We analyze the rich relaxation dynamics of Amoeba algorithms and demonstrate the existence of an unexpected scaling regime for the tree relaxation. Our results suggest that the equilibration time for Amoeba algorithms scales in general like N^{2}〈n_{lin}〉^{Δ}, where N denotes the number of tree nodes, 〈n_{lin}〉 the mean number of linear segments the trees are composed of, and Δ≃0.4.

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来源期刊

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.