Electromagnetic (EM) simulations have become an indispensable tool in the design of contemporary antennas. EM‐driven tasks, for example, parametric optimization, entail considerable computational efforts, which may be reduced by employing surrogate models. Yet, data‐driven modelling of antenna characteristics is largely hindered by the curse of dimensionality. This may be addressed using the recently reported domain‐confinement techniques, especially the nested‐kriging framework, which permits rendering of reliable surrogates over wide ranges of antenna parameters while greatly reducing the computational overhead of training data acquisition. Focused on modelling of multi‐band antennas, this paper attempts to reduce the cost of surrogate construction even further by incorporating variable‐fidelity simulations into the nested kriging. The principal challenge being design‐dependent frequency shifts between the models of various fidelities is handled through the development of a customized frequency scaling and output space mapping. Validation is carried out using a dual‐band dipole antenna modeled over broad ranges of operating conditions. A small training data set is sufficient to secure the predictive power comparable to that of the nested kriging model set up using solely high‐fidelity data, and by far exceeding the accuracy of conventional surrogates. Application examples for antenna optimization and experimental verification of the selected designs are also provided.

中文翻译：

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通过可变保真度电磁仿真和定制的空间映射，以经济高效的性能驱动的多频带天线建模

电磁（EM）模拟已成为现代天线设计中必不可少的工具。EM驱动的任务，例如参数优化，需要大量的计算工作，可以通过使用替代模型来减少这些工作。然而，尺寸特征的诅咒极大地阻碍了天线特性的数据驱动建模。可以使用最近报告的域限制技术（尤其是嵌套克里金框架）解决该问题，该技术允许在广泛的天线参数范围内渲染可靠的替代方案，同时大大减少训练数据获取的计算开销。本文着重于多频带天线的建模，试图通过将可变保真度仿真合并到嵌套克里金法中，进一步降低代建成本。主要挑战是通过开发定制的频率缩放和输出空间映射来解决各种保真度模型之间取决于设计的频移。使用在广泛的工作条件范围内建模的双频偶极天线进行验证。一个小的训练数据集就足以确保与仅使用高保真数据所建立的嵌套克里金模型相当的预测能力，远远超出了传统替代方法的准确性。还提供了针对所选设计的天线优化和实验验证的应用示例。使用在广泛的工作条件范围内建模的双频偶极天线进行验证。与仅使用高保真数据建立的嵌套克里金模型相比，一个小的训练数据集就足以确保其预测能力，并且远远超过了传统替代方法的准确性。还提供了针对所选设计的天线优化和实验验证的应用示例。使用在广泛的工作条件范围内建模的双频偶极天线进行验证。与仅使用高保真数据建立的嵌套克里金模型相比，一个小的训练数据集就足以确保其预测能力，并且远远超过了传统替代方法的准确性。还提供了针对所选设计的天线优化和实验验证的应用示例。