Rapid design centering of multi-band antennas using knowledge-based inverse models and response features

Accounting for manufacturing tolerances as well as uncertainties concerning operating conditions and material parameters is one of the important yet often neglected aspects of antenna development. Appropriate quantification of uncertainties allows for estimating the fabrication yield but also to carry out robust design (e.g., yield maximization). For reliability reasons, statistical analysis should be executed at the accuracy level of full-wave electromagnetic (EM) simulation. Unfortunately, the associated computational cost is normally unmanageable when using traditional methods, e.g., Monte Carlo (MC) simulation, in a brute force manner. Computationally tractable approaches are based on surrogate modeling techniques, where the fast metamodel is either used to run MC at low cost, or to directly extract statistical moments of the system output (e.g., polynomial chaos expansion). The bottleneck of surrogate-based frameworks is a potentially large number of training data samples necessary to render the surrogate, which may become problematic especially for higher-dimensional parameter spaces. This paper proposes a novel approach to design centering of multi-band antennas, which involves knowledge-based inverse regression models constructed at the level of appropriately-defined response features. Our methodology capitalizes on establishing – in​ the form of the inverse model – a functional relationship between the feature point coordinates affecting satisfaction of the prescribed design specification and geometry parameters of the antenna under analysis. The inverse model predicts the parameter vectors featuring improved likelihood of fulfilling the requirements under uncertainties. Due to low-dimensionality of the feature space, only a handful of EM analyses is necessary to render the model, which translates into a low cost of the entire design centering procedure.