When using Gaussian process (GP) machine learning as a surrogate model combined with the global optimization method for rapid optimization design of electromagnetic problems, a large number of covariance calculations are required, resulting in a calculation volume which is cube of the number of samples and low efficiency. In order to solve this problem, this study constructs a deep GP (DGP) model by using the structural form of convolutional neural network (CNN) and combining it with GP. In this network, GP is used to replace the fully connected layer of the CNN, the convolutional layer and the pooling layer of the CNN are used to reduce the dimension of the input parameters and GP is used to predict output, while particle swarm optimization (PSO) is used algorithm to optimize network structure parameters. The modeling method proposed in this paper can compress the dimensions of the problem to reduce the demand of training samples and effectively improve the modeling efficiency while ensuring the modeling accuracy. In our study, we used the proposed modeling method to optimize the design of a multiband microstrip antenna (MSA) for mobile terminals and obtained good optimization results. The optimized antenna can work in the frequency range of 0.69–0.96 GHz and 1.7–2.76 GHz, covering the wireless LTE 700, GSM 850, GSM 900, DCS 1800, PCS1900, UMTS 2100, LTE 2300, and LTE 2500 frequency bands. It is shown that the DGP network model proposed in this paper can replace the electromagnetic simulation software in the optimization process, so as to reduce the time required for optimization while ensuring the design accuracy.

中文翻译：

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基于高斯过程模型的天线优化设计

当使用高斯过程（GP）机器学习作为替代模型并结合全局优化方法来快速优化电磁问题的设计时，需要大量协方差计算，从而导致计算量为样本数的立方，而效率低下。为了解决这个问题，本研究通过使用卷积神经网络（CNN）的结构形式并将其与GP结合，构造了一个深度GP（DGP）模型。在此网络中，GP用于替换CNN的完全连接层，CNN的卷积层和池化层用于减小输入参数的维数，GP用于预测输出，而粒子群优化（ （PSO）用于优化网络结构参数的算法。本文提出的建模方法可以压缩问题的规模，减少训练样本的需求，在保证建模精度的同时，有效提高建模效率。在我们的研究中，我们使用提出的建模方法来优化移动终端的多频带微带天线（MSA）的设计，并获得了良好的优化结果。经过优化的天线可以在0.69–0.96 GHz和1.7–2.76 GHz的频率范围内工作，覆盖无线LTE 700，GSM 850，GSM 900，DCS 1800，PCS1900，UMTS 2100，LTE 2300和LTE 2500频段。结果表明，本文提出的DGP网络模型可以在优化过程中代替电磁仿真软件，从而在保证设计精度的同时，减少了优化所需的时间。