从带随机场的平均场模型中的雪崩到随机泊松分支事件的通用路径。

IF 2.7 2区 数学 Q1 MATHEMATICS, APPLIED
Chaos Pub Date : 2025-07-01 DOI:10.1063/5.0268639
Jordi Baró, Álvaro Corral
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引用次数: 0

摘要

平均场模型中的雪崩可以映射到定义通用性类的无记忆分支过程。在热力学极限下,我们给出了一种简化表达式,将广义临界和亚临界雪崩映射到随机发生时间的无记忆泊松分支过程中的根树。我们导出了非热随机场Ising模型和民主纤维束模型的精确映射,其中雪崩统计向临界发展,并作为滑移平均场理论中自组织临界的近似。三种模型的雪崩动力学和统计差异仅在于场密度、相互作用强度的演变,以及决定分支比的两项的乘积。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A universal route from avalanches in mean-field models with random fields to stochastic Poisson branching events.

Avalanches in mean-field models can be mapped to memoryless branching processes defining a universality class. We present a reduced expression mapping a broad family of critical and subcritical avalanches in mean-field models at the thermodynamic limit to rooted trees in a memoryless Poisson branching processes with random occurrence times. We derive the exact mapping for the athermal random field Ising model and the democratic fiber bundle model, where avalanche statistics progress toward criticality, and as an approximation for the self-organized criticality in slip mean-field theory. Avalanche dynamics and statistics in the three models differ only on the evolution of the field density, interaction strength, and the product of both terms determining the branching ratio.

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来源期刊
Chaos
Chaos 物理-物理:数学物理
CiteScore
5.20
自引率
13.80%
发文量
448
审稿时长
2.3 months
期刊介绍: Chaos: An Interdisciplinary Journal of Nonlinear Science is a peer-reviewed journal devoted to increasing the understanding of nonlinear phenomena and describing the manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines.
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