Fast and reliable knowledge-based design closure of antennas by means of iterative prediction-correction scheme

Abstract

Purpose A novel framework for expedited antenna optimization with an iterative prediction-correction scheme is proposed. The methodology is comprehensively validated using three real-world antenna structures: narrow-band, dual-band and wideband, optimized under various design scenarios. Design/methodology/approach The keystone of the proposed approach is to reuse designs pre-optimized for various sets of performance specifications and to encode them into metamodels that render good initial designs, as well as an initial estimate of the antenna response sensitivities. Subsequent design refinement is realized using an iterative prediction-correction loop accommodating the discrepancies between the actual and target design specifications. Findings The presented framework is capable of yielding optimized antenna designs at the cost of just a few full-wave electromagnetic simulations. The practical importance of the iterative correction procedure has been corroborated by benchmarking against gradient-only refinement. It has been found that the incorporation of problem-specific knowledge into the optimization framework greatly facilitates parameter adjustment and improves its reliability. Research limitations/implications The proposed approach can be a viable tool for antenna optimization whenever a certain number of previously obtained designs are available or the designer finds the initial effort of their gathering justifiable by intended re-use of the procedure. The future work will incorporate response features technology for improving the accuracy of the initial approximation of antenna response sensitivities. Originality/value The proposed optimization framework has been proved to be a viable tool for cost-efficient and reliable antenna optimization. To the knowledge, this approach to antenna optimization goes beyond the capabilities of available methods, especially in terms of efficient utilization of the existing knowledge, thus enabling reliable parameter tuning over broad ranges of both operating conditions and material parameters of the structure of interest.

Low-cost quasi-global optimization of expensive electromagnetic simulation models by inverse surrogates and response features

“…The experimental validation of the high-frequency structures utilized as verification case studies has been provided in their respective source papers 63 – 65 . Moreover, Structure I and II have been experimentally verified in our previous work, e.g., 66 , 67 . Thus, the experimental validation has not been provided as being immaterial to the scope of the paper.…”

Two-stage variable-fidelity modeling of antennas with domain confinement

“…Observe that all the benchmark antennas have been already validated, first, in their respective source papers 83 – 85 , and also in our previous work, e.g. 68 , 86 ). Therefore, the experimental validation has not been provided, as being immaterial to the scope of the paper.…”

A novel near field radiation shaping technique by using data driven surrogate based optimization for nondestructive hyperthermia

“…A viable and efficient solution for this problem is the usage of data driven surrogate models 19–23 . Application of data driven surrogate models for repetitive optimization process is a well‐known technique which is being studied for many different type of microwave and RF research topics such as; modeling of RF small signal microwave transistors, 24–28 design and optimization of passive microwave stages such as filters, 29–42 Meta surfaces, 43–45 and microwave antenna designs 46–57 …”

“…[19][20][21][22][23] Application of data driven surrogate models for repetitive optimization process is a well-known technique which is being studied for many different type of microwave and RF research topics such as; modeling of RF small signal microwave transistors, [24][25][26][27][28] design and optimization of passive microwave stages such as filters, [29][30][31][32][33][34][35][36][37][38][39][40][41][42] Meta surfaces, [43][44][45] and microwave antenna designs. [46][47][48][49][50][51][52][53][54][55][56][57] In this work, in order to achieve an efficient optimization process for focusing the EM energy into the tumor location, a data driven surrogate model of antenna array, which its data samples are generated using ARM method, is taken into the consideration. In the next sections, the ARM method and horn antenna array parametrization and scattering analysis will be studied.…”