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arXiv 1802.04201cond-mat.stat-mechphysics.bio-phphysics.comp-phq-bio.CB
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时间相关随机游动的元胞自动机模型:推导与分析

Cellular automaton models for time-correlated random walks: derivation and analysis

J. M. Nava-Sedeno, H. Hatzikirou, R. Klages, A. Deutsch

摘要:

自然界和社会中的许多扩散过程都是反常的,从根本上不同于传统的布朗运动。一个重要的例子是生物细胞的迁移,它表现出非平凡的时间衰减的速度自相关函数。这意味着相应的动力学特征是随着时间缓慢衰减的记忆效应。受此启发,我们构建非马尔可夫格气元胞自动机模型的记忆移动代理。为此目的,重新定向概率来自速度自相关函数,给出了先验;在这方面,我们的方法是“数据驱动”。我们考虑的具体例子是速度相关性,指数衰减或幂律,后者的功能产生异常扩散。元胞自动机的计算效率结合我们的分析结果铺平了道路,探索记忆和异常扩散的相互作用的细胞群体的动态,如汇合的细胞单层和细胞集群的相关性。

英文摘要:

Many diffusion processes in nature and society were found to be anomalous, in the sense of being fundamentally different from conventional Brownian motion. An important example is the migration of biological cells, which exhibits non-trivial temporal decay of velocity autocorrelation functions. This means that the corresponding dynamics is characterized by memory effects that slowly decay in time. Motivated by this we construct non-Markovian lattice-gas cellular automata models for moving agents with memory. For this purpose the reorientation probabilities are derived from velocity autocorrelation functions that are given a priori; in that respect our approach is `data-driven'. Particular examples we consider are velocity correlations that decay exponentially or as power laws, where the latter functions generate anomalous diffusion. The computational efficiency of cellular automata combined with our analytical results paves the way to explore the relevance of memory and anomalous diffusion for the dynamics of interacting cell populations, like confluent cell monolayers and cell clustering.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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