Behavioral modeling has been rising in importance in modern antenna design. It is primarily employed to diminish the computational cost of procedures involving massive full-wave electromagnetic (EM) simulations. Cheaper alternative offer surrogate models, yet, setting up data-driven surrogates is impeded by, among others, the curse of dimensionality. This article introduces a novel approach to reduced-cost surrogate modeling of antenna structures, which focuses the modeling process on design space regions containing high-quality designs, identified by randomized pre-screening. A supplementary dimensionality reduction is applied via the spectral analysis of the random observable set. The reduction process identifies the most important directions from the standpoint of geometry parameter correlations, and spans the domain along a small subset thereof. As demonstrated, domain confinement as outlined above permits a dramatic improvement of surrogate accuracy in comparison to the state-of-the-art modeling approaches.
Authors
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1007/978-3-031-36024-4_44
- Category
- Aktywność konferencyjna
- Type
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language
- angielski
- Publication year
- 2023
