Abstract
The paper proposes a novel framework for accelerated re-design (dimension scaling) of antenna structures using inverse surrogates. The major contribution of the work is a sensitivity-based model identification procedure, which permits a significant reduction of the number of reference designs required to render the surrogate. Rigorous formulation of the approach is supplemented by its comprehensive numerical validation using a triple-band uniplanar dipole antenna and a dual-band monopole antenna re-designed with respect to operating frequencies as well as the substrate parameters (thickness and dielectric permittivity). It is demonstrated that-for the considered test cases-the reliable inverse model can be set up using a significantly smaller (by a factor of three) number of reference points as compared to the original version of the method, whereas the dimension scaling process itself requires up to four EM simulations of the antenna structure.
Highlights
Full-wave electromagnetic (EM) analysis has become a mandatory tool in the design of contemporary antenna structures
This paper proposes an alternative approach to antenna re-design using inverse surrogates, where model identification is carried out using both the antenna responses and their sensitivities at the reference designs
The originality and the technical contributions of the work include (i) incorporation of sensitivities into the inverse surrogate model for rapid dimension scaling of antenna structures, (ii) development of a rigorous procedure for low-cost estimation of geometry parameter gradients with respect to the antenna performance figures, (iii) implementation of the sensitivity-based re-design framework which is demonstrably superior over the non-sensitivity versions in terms of a dramatic reduction of the number of reference designs required to set up the surrogate model
Summary
Full-wave electromagnetic (EM) analysis has become a mandatory tool in the design of contemporary antenna structures. The originality and the technical contributions of the work include (i) incorporation of sensitivities into the inverse surrogate model for rapid dimension scaling of antenna structures, (ii) development of a rigorous procedure for low-cost estimation of geometry parameter gradients with respect to the antenna performance figures (required for inverse model identification), (iii) implementation of the sensitivity-based re-design framework which is demonstrably superior over the non-sensitivity versions in terms of a dramatic (by a factor fo three) reduction of the number of reference designs required to set up the surrogate model. Reducing the number of required designs is highly desirable
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