Simulation-Driven Antenna Design Using Surrogate-Based Optimization

Abstract

Accurate responses of antennas, in many cases, can be obtained only with discrete full-wave electromagnetic (EM) simulations. Therefore, contemporary antenna design strongly relies on these EM simulations. On the other hand, direct use of high-fidelity EM simulations in the design process, particularly for automated parameter optimization, often results in prohibitive computational costs. In this chapter, we illustrate how the designs of various antennas can be obtained efficiently using an automated surrogate-based optimization (SBO) methodology. The SBO techniques considered here include the adaptive design specification technique, variable-fidelity simulation-driven optimization, and shape-preserving response prediction. The essence of these techniques resides in shifting the optimization burden to a fast surrogate of the antenna structure, and using coarse-discretization EM models to configure the surrogate. A properly created and handled surrogate serves as a reliable prediction tool allowing satisfactory designs to be obtained at the cost of a few simulations of the high-fidelity antenna model. We also demonstrate the effect of the coarse-discretization model fidelity on the final design quality and the computational cost of the design process. Finally, we give an example of automatic management of the coarse model quality. Recommendations concerning the application of specific SBO techniques to antenna design are also presented.