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拉普拉斯生长分形维数的通用维数函数。

A universal dimensionality function for the fractal dimensions of Laplacian growth.

作者信息

Nicolás-Carlock J R, Carrillo-Estrada J L

机构信息

Instituto de Física, Benemérita Universidad Autónoma de Puebla, Puebla, 72570, Mexico.

出版信息

Sci Rep. 2019 Feb 4;9(1):1120. doi: 10.1038/s41598-018-38084-3.

Abstract

Laplacian growth, associated to the diffusion-limited aggregation (DLA) model or the more general dielectric-breakdown model (DBM), is a fundamental out-of-equilibrium process that generates structures with characteristic fractal/non-fractal morphologies. However, despite diverse numerical and theoretical attempts, a data-consistent description of the fractal dimensions of the mass-distributions of these structures has been missing. Here, an analytical model of the fractal dimensions of the DBM and DLA is provided by means of a recently introduced dimensionality equation for the scaling of clusters undergoing a continuous morphological transition. Particularly, this equation relies on an effective information-function dependent on the Euclidean dimension of the embedding-space and the control parameter of the system. Numerical and theoretical approaches are used in order to determine this information-function for both DLA and DBM. In the latter, a connection to the Rényi entropies and generalized dimensions of the cluster is made, showing that DLA could be considered as the point of maximum information-entropy production along the DBM transition. The results are in good agreement with previous theoretical and numerical estimates for two- and three-dimensional DBM, and high-dimensional DLA. Notably, the DBM dimensions conform to a universal description independently of the initial cluster-configuration and the embedding-space.

摘要

拉普拉斯生长与扩散限制凝聚(DLA)模型或更一般的介电击穿模型(DBM)相关,是一种基本的非平衡过程,它能生成具有特征性分形/非分形形态的结构。然而,尽管有各种数值和理论尝试,但对于这些结构质量分布的分形维数,一直缺少与数据一致的描述。在此,通过最近引入的用于描述经历连续形态转变的团簇标度的维数方程,给出了DBM和DLA分形维数的解析模型。特别地,该方程依赖于一个有效信息函数,它取决于嵌入空间的欧几里得维数和系统的控制参数。为了确定DLA和DBM的这个信息函数,采用了数值和理论方法。在DBM中,建立了与团簇的雷尼熵和广义维数的联系,表明DLA可被视为沿DBM转变的最大信息熵产生点。结果与之前关于二维和三维DBM以及高维DLA的理论和数值估计高度吻合。值得注意的是,DBM维数符合一个通用描述,与初始团簇构型和嵌入空间无关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fb/6362037/6716b34e9192/41598_2018_38084_Fig3_HTML.jpg

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