Dimensionality-reduced antenna modeling with stochastically established constrained domain

Abstract

Over the recent years, surrogate modeling methods have become increasingly widespread in the design of contemporary antenna systems. On the one hand, it is associated with a growing awareness of numerical optimization, instrumental in achieving high-performance structures. On the other hand, considerable computational expenses incurred by massive full-wave electromagnetic (EM) analyses, routinely employed as a major design tool, foster the development of novel design techniques that exhibit practically acceptable costs while ensuring reliability. In this context, substituting EM simulations by fast surrogates is a profitable solution. Data-driven modeling is arguably the most popular approach owing to its versatility and the abundance of specific methods. Yet, a construction of approximation surrogates is severely encumbered by the curse of dimensionality, and even more so by the broad ranges of material and geometry parameters the model should cover to be applicable for solving practical design tasks. The recently reported performance-driven modeling paradigm offers workaround these obstacles by restricting the surrogate rendition to a small section of the parameter space, containing designs of sufficiently high quality according to performance requirements imposed on the system under study. Nevertheless, identification of this region is based on database designs that have to be pre-optimized, which is associated with significant CPU expenses. The usage of the reference designs can be replaced by stochastic domain identification, leading to considerable computational savings. This paper introduces a further advancement, where the metamodel domain is outlined based on the spectral analysis of the random observables pre-selected using an automated decision-making process. Our procedure retains the benefits of the prior techniques but also reduces the domain dimensionality, which translates into additional cost reduction of training data acquisition. These have been conclusively demonstrated through numerical validation involving three microstrip antennas and comprehensive comparisons with six state-of-the-art benchmark techniques.