arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2501.03705cond-mat.stat-mech
收藏

极值自组织临界模型中的关联时间

Correlation time in extremal self-organized critical models

Rahul Chhimpa, Abha Singh, Avinash Chand Yadav

AI总结:

本文结合有限尺寸标度与极值理论,数值研究 Bak-Sneppen 与 Robin Hood 模型的关联时间统计,揭示其幂律标度及接近 Gumbel 的广义极值分布。

摘要:

我们通过数值方法研究了极值自组织临界模型中的关联时间,即 Bak-Sneppen 演化模型和 Robin Hood 动力学。所谓(适应度)关联时间,是指在临界状态下,物种在整个空间区域内灭绝或突变所需的持续时间。我们运用有限尺寸标度和极值理论方法来理解关联时间的统计特性。我们发现关联时间的均值、方差、众数和峰值概率均呈现幂律形式的系统尺寸标度行为。我们实现了关联时间累积概率分布的数据塌缩,且标度函数服从接近 Gumbel 函数的广义极值密度。

英文摘要:

We investigate correlation time numerically in extremal self-organized critical models, namely, the Bak-Sneppen evolution and the Robin Hood dynamics. The (fitness) correlation time is the duration required for the extinction or mutation of species over the entire spatial region in the critical state. We apply the methods of finite-size scaling and extreme value theory to understand the statistics of the correlation time. We find power-law system size scaling behaviors for the mean, the variance, the mode, and the peak probability of the correlation time. We obtain data collapse for the correlation time cumulative probability distribution, and the scaling function follows the generalized extreme value density close to the Gumbel function.

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。

↑