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arXiv 1001.2880cond-mat.stat-mech
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模拟反常扩散和非广延统计分布的自相似运动

Self-similar motion for modeling anomalous diffusion and nonextensive statistical distributions

Zhifu Huang, Guozhen Su, Qiuping A Wang, Jincan Chen

摘要:

我们引入了一个新的普适类的一维迭代模型产生的自相似运动,其中的Feigenbaum常数被推广为自相似率,并可以预先确定。均方位移随时间的变化曲线表明,这种运动是一种反常扩散,扩散系数依赖于自相似速率。此外,还发现位移的分布在某些情况下符合q-高斯型分布,在某些情况下符合双峰分布,具有可靠的精度。结果表明,自相似运动可以用来描述反常扩散和非广延统计分布。

英文摘要:

We introduce a new universality class of one-dimensional iteration model giving rise to self-similar motion, in which the Feigenbaum constants are generalized as self-similar rates and can be predetermined. The curves of the mean-square displacement versus time generated here show that the motion is a kind of anomalous diffusion with the diffusion coefficient depending on the self-similar rates. In addition, it is found that the distribution of displacement agrees to a reliable precision with the q-Gaussian type distribution in some cases and bimodal distribution in some other cases. The results obtained show that the self-similar motion may be used to describe the anomalous diffusion and nonextensive statistical distributions.

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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