Abstract

Fast replacement models have been playing an increasing role in high-frequency electronics, including the design of antenna structures. Their role is to improve computational efficiency of the procedures that normally entail large numbers of expensive full-wave electromagnetic (EM) simulations, e.g., parametric optimization or uncertainty quantification. Recently introduced performance-driven modeling methods, such as the nested kriging framework, alleviate some of the common difficulties pertinent to conventional modeling methods. These include the curse of dimensionality but also the need for rendering models to be valid for broad ranges of antenna parameters and operating conditions, as dictated by the design utility of the surrogates. The keystone of performance-driven methods is an appropriate confinement of the model domain so that the training data is only acquired in the regions containing high-quality designs. Identification of such regions is realized using a set of so-called reference designs pre-optimized for selected ensembles of performance requirements. The CPU cost of generating the reference points may be considerable and compromise the savings obtained by operating in a constrained domain. In this paper, a technique for automated, reliable and low-cost acquisition of the reference designs is proposed. Our methodology involves inverse sensitivities, iterative correction procedures, and accelerated feature-based gradient search with sparse Jacobian updates. It is validated using three microstrip antenna examples and demonstrated as an efficient tool for lowering the cost of building surrogate models within the nested kriging framework. The intended use of our approach is expedited construction of database designs for constrained modeling frameworks, construction of inverse surrogates as well as procedures for rapid re-design and dimension scaling of antenna structures.

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