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Low-cost surrogate modeling of antennas using two-level Gaussian process regression method
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields ( IF 1.6 ) Pub Date : 2021-04-27 , DOI: 10.1002/jnm.2886
Zhen Zhang 1, 2 , Fan Jiang 1, 3 , Yaxi Jiao 1 , Qingsha S. Cheng 1
Affiliation  

In order to improve the accuracy of the surrogate model for antennas, a novel two-level Gaussian process regression (GPR) modeling method is proposed in this paper. A heuristic hypercube sampling method is proposed using the K-means clustering method to generate the training dataset with high uniformity. Based on the training dataset, the first-level GPR model is established between the design parameters and the full-wave electromagnetic (EM) simulation responses. The second-level GPR model is established using the design parameters and the residuals between the first-level GPR model and the EM simulation model. The sum of the two surrogate models is the two-level GPR model. The performance of the proposed modeling method is verified by two antenna examples including an ultra-wideband antenna and a circularly polarized dielectric antenna. Numerical results show that the proposed two-level GPR method achieves higher accuracy of antenna models than the conventional methods (GPR method and neural networks) with no additional cost. The overall time saving of the proposed method compared to the conventional methods is more than 50% for the majority of our tests.

中文翻译:

使用两级高斯过程回归方法对天线进行低成本代理建模

为了提高天线代理模型的精度,本文提出了一种新的两级高斯过程回归(GPR)建模方法。提出了一种启发式超立方体采样方法,使用K-means聚类方法生成具有高均匀性的训练数据集。基于训练数据集,在设计参数和全波电磁(EM)仿真响应之间建立一级探地雷达模型。二级探地雷达模型是利用设计参数和一级探地雷达模型与电磁仿真模型之间的残差建立的。两个代理模型的总和就是两级探地雷达模型。所提出的建模方法的性能通过两个天线示例(包括超宽带天线和圆极化电介质天线)进行了验证。数值结果表明,所提出的两级探地雷达方法比传统方法(探地雷达方法和神经网络)实现了更高的天线模型精度,且无需额外成本。
更新日期:2021-04-27
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