Numerical Prediction of the Steady-State Distribution Under Stochastic Resetting from Measurements

IF 1.3 3区 物理与天体物理 Q3 PHYSICS, MATHEMATICAL
Ron Vatash, Amy Altshuler, Yael Roichman
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引用次数: 0

Abstract

A common and effective method for calculating the steady-state distribution of a process under stochastic resetting is the renewal approach that requires only the knowledge of the reset-free propagator of the underlying process and the resetting time distribution. The renewal approach is widely used for simple model systems such as a freely diffusing particle with exponentially distributed resetting times. However, in many real-world physical systems, the propagator, the resetting time distribution, or both are not always known beforehand. In this study, we develop a numerical renewal method to determine the steady-state probability distribution of particle positions based on the measured system propagator in the absence of resetting combined with the known or measured resetting time distribution. We apply and validate our method in two distinct systems: one involving interacting particles and the other featuring strong environmental memory. Thus, the renewal approach can be used to predict the steady state under stochastic resetting of any system, provided that the free propagator can be measured and that it undergoes complete resetting.

测量随机重置下稳态分布的数值预测
计算随机重置下过程稳态分布的一种常用而有效的方法是更新法,它只需要了解底层过程的无重置传播量和重置时间分布。更新方法被广泛用于简单的模型系统,如具有指数分布的重置时间的自由扩散粒子。然而,在许多现实世界的物理系统中,传播器、重置时间分布或两者并不总是事先知道的。在这项研究中,我们开发了一种数值更新方法来确定粒子位置的稳态概率分布,该方法基于测量的系统传播子在没有重置的情况下结合已知或测量的重置时间分布。我们在两个不同的系统中应用并验证了我们的方法:一个涉及相互作用的粒子,另一个具有强环境记忆。因此,更新方法可以用于预测任意系统在随机重置下的稳态,只要自由传播子可以测量并且经过完全重置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Statistical Physics
Journal of Statistical Physics 物理-物理:数学物理
CiteScore
3.10
自引率
12.50%
发文量
152
审稿时长
3-6 weeks
期刊介绍: The Journal of Statistical Physics publishes original and invited review papers in all areas of statistical physics as well as in related fields concerned with collective phenomena in physical systems.
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