大偏差函数评价中的瞬时和有限尺寸标度：用生灭过程的分析研究
Finite-time and finite-size scalings in the evaluation of large-deviation functions: Analytical study using a birth-death process
摘要：
Giardinà-Kurchan-Peliti算法是一种数值方法，它使用种群动力学来计算与时间平均观测值分布相关的大偏差函数。为了研究该算法的数值误差，我们明确设计了一个随机生灭过程，描述人口概率的时间演化。从这个公式中，我们推导出算法的系统误差与种群大小的倒数成比例地减少。基于这一观察，我们提出了一个简单的插值技术更好地估计大偏差函数。我们提出的方法是详细明确的两个状态模型。
英文摘要：
The Giardinà-Kurchan-Peliti algorithm is a numerical procedure that uses population dynamics in order to calculate large deviation functions associated to the distribution of time-averaged observables. To study the numerical errors of this algorithm, we explicitly devise a stochastic birth-death process that describes the time evolution of the population probability. From this formulation, we derive that systematic errors of the algorithm decrease proportionally to the inverse of the population size. Based on this observation, we propose a simple interpolation technique for the better estimation of large deviation functions. The approach we present is detailed explicitly in a two-state model.
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。
