马尔科夫蒙特卡洛
元建模
克里金
计算机科学
替代模型
蒙特卡罗方法
可靠性(半导体)
贝叶斯概率
忠诚
采样(信号处理)
重要性抽样
算法
数学优化
数据挖掘
机器学习
数学
人工智能
统计
电信
功率(物理)
物理
滤波器(信号处理)
量子力学
计算机视觉
程序设计语言
作者
Bowen Li,Bingyi Li,Xiang Jia,Zhiwei Cheng,Bo Guo
标识
DOI:10.1109/icrms55680.2022.9944593
摘要
Since the physical structure and mathematical models are more complex, reliability analysis in practical engineering can be expensive and difficult. A two-level multifidelity metamodel method for reliability analysis is introduced. Following the surrogate model in most of the relevant works, low-fidelity data and high-fidelity data are integrated by co-Kriging model. Besides, the co-Kriging model also provide an approximation for initial performance function. Bayesian method is adopted in model solution and a hybrid Markov chain Monte Carlo (MCMC) sampling algorithm is proposed. High-fidelity response of reliability performance function is estimated by the conditional distribution derivation based on Bayesian theory. Failure domain is identified by indicator function in sampling space that consists of samples derived from MCMC. Accordingly, failure probability estimations are obtained using Monte Carlo simulation (MCS). It is demonstrated through an illustrative example that the proposed method is valid and accurate.
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