Abstract

The design of antenna systems poses a significant challenge due to stringent per-formance requirements dictated by contemporary applications and the high com-putational costs associated with models, particularly full-wave electromagnetic (EM) analysis. Presently, EM simulation plays a crucial role in all design phases, encompassing topology development, parametric studies, and the final adjustment of antenna dimensions. The latter stage is especially critical as rigorous numerical optimization becomes essential for achieving optimal performance. In an increas-ing number of instances, global parameter tuning is necessary. Unfortunately, the use of nature-inspired algorithms, the prevalent choice for global design, is hin-dered by their poor computational efficiency. This article presents an innovative approach to cost-efficient global optimization of antenna input characteristics. Our methodology leverages response feature technology, ensuring inherent regulariza-tion of the optimization task by exploring the nearly-linear dependence between the coordinates of feature points and the antenna's dimensions. The optimization process is structured as a machine learning (ML) procedure, utilizing a kriging surrogate model rendering response features to generate promising candidate de-signs (infill points). This model is iteratively refined using accumulated EM simulation data. Further acceleration is achieved by incorporating multi-fidelity EM analysis, where initial sampling and surrogate model construction use low-fidelity EM simulations, and the ML optimization loop employs high-fidelity EM analysis. The multi-fidelity EM simulation data is blended into a single surrogate using co-kriging. Extensive verification of the presented algorithm demonstrates its remarkable computational efficiency, with an average running cost not exceed-ing ninety EM simulations per run and up to a seventy percent relative speedup over the single-fidelity procedure.

Citations

Authors (3)