Accelerated parameter tuning of antenna structures using inverse and feature‐based forward kriging surrogates

Abstract

AbstractTuning of geometry parameters is one of the essential stages of contemporary antenna design. It is necessary because available design methods, whether based on theoretical considerations or on engineering experience, are only capable of yielding initial designs that need further adjustment in order to boost the performance parameters as much as possible. Numerical optimization is also imperative for antenna re‐design with respect to different operating conditions (e.g., center frequency) or material parameters (e.g., substrate permittivity/thickness). As the tuning process normally involves full‐wave electromagnetic (EM) analysis, it may incur considerable computational expenses. Reducing these costs is highly desirable from the perspective of both academic research and industry. This paper proposes a simple surrogate‐assisted framework for accelerated antenna parameter tuning that allows for reusing the previously acquired design data. The first (inverse) surrogate serves as a reliable predictor for generating a reasonable initial design, whereas the second (forward) model encodes antenna sensitivities at the level of so‐called response features. The involvement of the response feature technology leads to a more accurate rendition of the antenna gradients, which speeds up the design refinement as compared to the forward model constructed at the level of original antenna characteristics. Our methodology is demonstrated using two microstrip antennas and compared to the previously reported warm‐start optimization procedures.