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arXiv 2502.03225cond-mat.stat-mech
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嵌套随机重置:非平衡稳态与精确关联

Nested Stochastic Resetting: Nonequilibrium Steady-states and Exact Correlations

Henry Alston, Callum Britton, Thibault Bertrand

AI总结:

本文研究单向相互作用的嵌套随机重置扩散链,解析推导出各过程稳态分布、矩及精确两点关联,为多粒子非平衡系统关联提供可解框架。

摘要:

随机重置破坏细致平衡,并驱动非平衡稳态的形成。在此,我们考虑一条扩散过程链 $x_i(t)$,它们单向相互作用:在随机时间间隔内,过程 $x_n$ 随机重置为 $x_{n-1}$ 的瞬时值。我们解析地推导出这些嵌套随机重置过程的稳态统计量,包括每个过程的稳态分布及其矩。我们还能够通过将该问题映射为随机计数过程的排序统计问题,精确计算过程之间的稳态两点关联 $\langle x_n x_{n+j}\rangle$。理解多粒子非平衡系统中的统计量与关联仍然是一项艰巨的挑战,而我们的结果提供了此类可精确处理的关联的一个实例。我们期望该框架既有助于为具有单向相互作用的随机过程建立一个与模型无关的框架,又能找到直接应用,例如在有损信息传播的建模中。

英文摘要:

Stochastic resetting breaks detailed balance and drives the formation of nonequilibrium steady states . Here, we consider a chain of diffusive processes $x_i(t)$ that interact unilaterally: at random time intervals, the process $x_n$ stochastically resets to the instantaneous value of $x_{n-1}$. We derive analytically the steady-state statistics of these nested stochastic resetting processes including the stationary distribution for each process as well as its moments. We are also able to calculate exactly the steady-state two-point correlations $\langle x_n x_{n+j}\rangle$ between processes by mapping the problem to one of the ordering statistics of random counting processes. Understanding statistics and correlations in many-particle nonequilibrium systems remains a formidable challenge and our results provide an example of such tractable correlations. We expect this framework will both help build a model-independent framework for random processes with unilateral interactions and find immediate applications, e.g. in the modelling of lossy information propagation.

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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