Importance Sampling for Reliability Evaluation With Stochastic Simulation Models
Importance Sampling for Reliability Evaluation With Stochastic Simulation Models
复制标题
使用随机仿真模型进行可靠性评估的重要性采样
DOI：
10.1080/00401706.2014.1001523
复制
发表时间：
2015
期刊：
影响因子：
2.5
通讯作者：
Nan Chen
中科院分区：
文献类型：
--
作者：
Youngjun Choe;E. Byon;Nan Chen
重要性抽样被用来提高模拟的效率，其中模拟输出在给定固定输入的情况下是唯一确定的。我们将重要性抽样理论推广到用随机模拟来估计系统的可靠性。由于计算能力的提高，随机模拟模型在许多应用中被用来描述复杂的系统行为。随机模拟模型在相同的输入下产生随机输出。在给定预算约束的情况下，我们提出了一种新的方法，称为随机重要性抽样，它有效地利用了具有未知输出分布的随机模拟。具体地说，我们得到了最小化估计量方差的最优重要抽样密度和分配方法。在计算密集型气动弹性风力机仿真中的应用证明了该方法的有效性。这篇文章的补充材料可以在网上找到。
Importance sampling has been used to improve the efficiency of simulations where the simulation output is uniquely determined, given a fixed input. We extend the theory of importance sampling to estimate a system’s reliability with stochastic simulations. Thanks to the advance of computing power, stochastic simulation models are employed in many applications to represent a complex system behavior. A stochastic simulation model generates stochastic outputs at the same input. Given a budget constraint on total simulation replications, we develop a new approach, which we call stochastic importance sampling, which efficiently uses stochastic simulations with unknown output distribution. Specifically, we derive the optimal importance sampling density and allocation procedure that minimize the variance of an estimator. Application to a computationally intensive aeroelastic wind turbine simulation demonstrates the benefits of the proposed approach. Supplementary materials for this article are available online.