Design of modern antenna structures heavily depends on electromagnetic (EM) simulation tools. EM analysis provides reliable evaluation of increasingly complex designs but tends to be CPU intensive. When multiple simulations are needed (e.g., for parameters tuning), the aggregated simulation cost may become a serious bottleneck. As one possible way of mitigating the issue, the recent literature fosters utilization of faster representations, or surrogates, of the system at hand. Notwithstanding, conventional models are severely affected by the curse of dimensionality. In practice, modelling of antenna structures described by no more than a few parameters over narrow parameter ranges is possible. In the context of the structural complexity of modern antennas, this is hardly acceptable. This paper presents a novel technique for cost-efficient design-oriented modelling of multi-band antennas. Our approach integrates a recently reported nested kriging framework and the response feature technology. This combination enables rendering of reliable surrogates valid within broad ranges of geometry parameters while using small training data sets. Benchmarking against conventional modelling methods demonstrates superiority of the proposed framework in terms of both the predictive power of the surrogate and its setup cost. Design applications for antenna optimization are discussed as well.
Authors
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1016/j.aeue.2020.153202
- Category
- Publikacja w czasopiśmie
- Type
- artykuły w czasopismach
- Language
- angielski
- Publication year
- 2020
