Monte Carlo simulation using support vector machine and kernel density for failure probability estimation
Monte Carlo simulation using support vector machine and kernel density for failure probability estimation
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DOI：
10.1016/j.ress.2021.107481
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Seunggyu Lee
中科院分区：
文献类型：
--
作者：
Seunggyu Lee
蒙特卡罗模拟需要大量的采样点。在蒙特卡罗模拟中，计算所有采样点的性能函数以确定设计的失败。如果性能函数的计算涉及大型数值模型，巨大的数值成本是不可避免的。在本研究中，应用支持向量机作为性能函数的元模型来克服这个缺点。支持向量机的核密度和修改余量用于支持向量机的主动学习。以支持向量机修正余量在设计空间中所占的比例作为主动学习结束的标准。所提出的方法应用于一些数值例子并进行了检验。
Monte Carlo simulation requires a large number of sampling points. In a Monte Carlo simulation, the performance function is calculated for all sampling points to determine the failure of the design. If the calculation of the performance function involves large numerical models, a tremendous numerical cost is inevitable. In this study, a support vector machine was applied as a metamodel of the performance function to overcome this drawback. Kernel density and a modified margin of the support vector machine were used for the active learning of the support vector machine. The proportion of the support vector machine's modified margin in the design space was applied as the criterion to end active learning. The proposed method is applied to some numerical examples and examined.