Data-Driven Arbitrary Polynomial Chaos for Uncertainty Quantification in Filters

被引：0
|
作者：
Alkhateeb, Osama J. [1 ]
Ida, Nathan [1 ]
机构：
[1] Univ Akron, Dept Elect & Comp Engn, Akron, OH 44325 USA
来源：
APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL | 2018年 / 33卷 / 09期
关键词：
Data-driven arbitrary polynomial chaos; generalized polynomial chaos; Monte Carlo sampling; uncertainty quantification; STOCHASTIC DIFFERENTIAL-EQUATIONS;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A non-intrusive arbitrary polynomial chaos (aPC) method is applied to a problem of a band-stop filter with geometrical imperfections. The construction of aPC scheme only requires evaluating a finite number of moments, and does not involve assigning analytical probability density functions for the uncertain parameters of a stochastic model. Therefore, aPC is well suited for applications where the uncertain parameters are represented by raw data samples, as with the case of experimental measurements. The numerical examples show that the aPC approach is accurate even with a limited number of input samples.
引用
收藏
页码：1048 / 1051
页数：4
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