Reliable Microwave Modeling by Means of Variable-Fidelity Response Features

Abstract

In this work, methodologies for low-cost and reliable microwave modeling are presented using variable-fidelity response features. The two key components of our approach are: 1) a realization of the modeling process at the level of suitably selected feature points of the responses (e.g., $S$ -parameters versus frequency) of the structure at hand and 2) the exploitation of variable-fidelity EM simulation data, also for the response feature representation. Due to the less nonlinear dependence between the coordinates of the feature points on the geometrical parameters of the structure of interest, the amount of training data can be greatly reduced. Additional cost reduction is obtained by means of generating the majority of the training data at a coarse-discretization EM simulation level and exploiting the correlations between the EM models of various fidelities. We propose two ways of combining the low- and high-fidelity data sets: 1) an external approach, through space mapping (simpler to implement) and 2) an internal approach, using co-kriging (more flexible and potentially offering better accuracy). The operation and performance of our modeling techniques are demonstrated by three microstrip filter examples and a compact rat-race coupler. A comprehensive verification and comparisons with several benchmark techniques, as well as application examples (filter optimization) are also provided.