一种在存在可变性的情况下推断网络s参数的机器学习方法

Xiao Ma, M. Raginsky, A. Cangellaris
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引用次数: 3

摘要

本文提出使用变分自编码器(一种生成建模技术)来推断存在制造可变性的线性多端口网络的s参数问题。变分自编码器学习底层数据生成过程,并产生一个可以近似模拟训练数据概率分布的生成网络。生成的样本可用于后续的统计模拟。后处理步骤,将向量拟合应用于预测的s参数,将模型约束为有限阶有理函数形式并强制执行适当的物理约束。通过对耦合微带传输线的实际应用,验证了该方法的有效性。
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A machine learning methodology for inferring network S-parameters in the presence of variability
This paper proposes the use of Variational Autoencoders, a generative modeling technique, for the problem of inferring S-parameters of linear multiport networks in the presence of manufacturing variability. The Variational Autoencoder learns the underlying data generation process and yields a generative network that can approximately mimic the probability distribution of the training data. The generated samples can be used for subsequent statistical simulations. A post-processing step, applying Vector Fitting to the predicted S-parameters, constrains the model to a finite-order rational function form and enforces appropriate physical constraints. The method is validated through its application to a coupled micro strip transmission line.
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