Knowledge-based performance-driven modeling of antenna structures

摘要

The importance of surrogate modeling techniques in the design of modern antenna systems has been continuously growing over the recent years. This phenomenon is a matter of practical necessity rather than simply a fashion. On the one hand, antenna design procedures rely on full-wave electromagnetic (EM) simulation tools. On the other hand, the computational costs incurred by repetitive EM analyses involved in solving common tasks (parameter tuning, uncertainty quantification, multi-criterial design, etc.), are often prohibitive. The replacement of full-wave simulations by fast surrogates may mitigate these issues; as a matter of fact, it is the only viable option for carrying out EM-driven design in many cases. Among available modeling approaches, data-driven surrogates are by far the most popular due to their accessibility and versatility. At the same time, a construction of reliable models is hindered by the curse of dimensionality, high nonlinearity of antenna characteristics, as well as broad ranges of parameters and operating conditions that the model has to cover to ensure its design utility. Recently proposed performance-driven modeling frameworks offer a workaround these issues by restricting the model domain to the parameter space regions that contain high-quality designs (w.r.t. the assumed performance metrics). However, the domain determination requires acquisition of a set of pre-optimized reference designs, which adds to the overall computational cost of the surrogate model setup in a significant manner. This work proposes a novel two-stage knowledge-based approach, where the confined domain is defined without using any reference designs. Instead, a preselected set of random observables is employed to establish an inverse regression model being a basis for domain determination of the final surrogate. Comprehensive numerical validation involving three antenna structures indicates that our methodology offers the computational benefits similar to those of the previous performance-driven methods while considerably reducing the initial setup cost, by a factor of sixty percent on the average, which has been achieved by exploiting the problem-specific knowledge.