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Algorithms for the Laplace–Stieltjes Transforms of First Return Times for Stochastic Fluid Flows

机译:随机流体初次返回时间的Laplace-Stieltjes变换算法

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We derive several algorithms, including quadratically convergent algorithms, which can be used to calculate the Laplace–Stieltjes transforms of the time taken to return to the initial level in the Markovian stochastic fluid flow model. We give physical interpretations of the algorithms and consider their numerical analysis. The numerical performance of the algorithms, which depends on the physical properties of the process, is discussed and illustrated with simple examples. Besides the powerful algorithms, this paper contributes interesting theoretical results. In particular, the methodology for constructing these algorithms is a valuable contribution to the theory of fluid flow models. Moreover, useful physical interpretations of the algorithms, and related expressions, given in terms of the fluid flow model, can assist in further analysis and help in a better understanding of the model.
机译:我们推导了几种算法,包括二次收敛算法,可用于计算马尔可夫随机流体模型中返回初始水平所需时间的Laplace-Stieltjes变换。我们对算法进行物理解释,并考虑其数值分析。通过简单的示例来讨论和说明算法的数值性能,该性能取决于过程的物理属性。除了强大的算法外,本文还提供了有趣的理论结果。特别地,用于构造这些算法的方法学对流体流动模型的理论做出了宝贵的贡献。此外,就流体模型而言,对算法的有用物理解释以及相关表达式可以帮助进行进一步的分析并有助于更好地理解模型。

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