Appraisal of enhanced surrogate models for substrate integrate waveguide devices characterization

Nowadays the use of surrogate models (SMs) is becoming a common
practice to accelerate the optimization phase of the design of microwave and millimeter
wave devices. In order to further enhance the performances of the optimization
process, the accuracy of the response provided by a SM can be improved employing
a suitable output correction block, obtaining in this way a so-called enhanced
surrogate model (ESM). In this paper a comparative study of three different
techniques for building ESMs, i.e. Kriging, Support Vector Regression Machines
(SVRMs) and Artificial Neural Networks (ANNs), applied to the modelling of substrate
integrated waveguide (SIW) devices, is presented and discussed.