Machine Learning based Surrogate Modeling for Wave Impedances in
Rectangular Dielectric Waveguides
Even though the behavior of rectangular dielectric
waveguides (RDWGs) can be evaluated accurately using
numerical simulations, limited time and computational resources
generally do not allow an extensive exploration of the design
space. Thus, in this paper we present a way to develop a
fast-to-evaluate surrogate model for the wave impedance of the
fundamental mode HE11 in RDWGs at frequencies between
134 and 141 GHz. To this end, gaussian process regression
(GPR) with a customized covariance function as well as artificial
neural networks (ANNs) and polynomial regression (PR) are
applied. Compared by the mean absolute deviation (MAD),
our finally proposed model shows much better agreement
with numerical solutions than Maractili’s famous approximate
analytical approach (MA). In contrast to MA, our approach is
furthermore applicable to DWGs with arbitrary cross-sections.