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.

中文翻译：

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通过迭代预测-校正方案快速可靠的基于知识的天线设计闭合

目的

提出了一种具有迭代预测-校正方案的用于加速天线优化的新框架。该方法使用三种实际天线结构进行了全面验证：窄带、双带和宽带，并在各种设计场景下进行了优化。

设计/方法/方法

所提出方法的关键是重用针对各种性能规格集预先优化的设计，并将它们编码到元模型中，以呈现良好的初始设计以及天线响应灵敏度的初始估计。随后的设计改进是使用迭代预测-校正循环来实现的，以适应实际和目标设计规范之间的差异。

发现

所提出的框架能够以几个全波电磁仿真为代价产生优化的天线设计。迭代校正程序的实际重要性已通过对仅梯度细化的基准测试得到证实。已经发现，将特定问题的知识纳入优化框架极大地促进了参数调整并提高了其可靠性。

研究限制/影响

只要有一定数量的先前获得的设计可用，或者设计人员发现他们收集的初步努力通过预期的程序重用是合理的，所提出的方法就可以成为天线优化的可行工具。未来的工作将结合响应特征技术，以提高天线响应灵敏度初始近似的准确性。

原创性/价值

所提出的优化框架已被证明是一种具有成本效益且可靠的天线优化的可行工具。据了解，这种天线优化方法超出了现有方法的能力，特别是在有效利用现有知识方面，从而能够在感兴趣的结构的广泛工作条件和材料参数范围内实现可靠的参数调整。