A common approach in the literature when obtaining surrogate models of reflectarray unit cells is to include, among other variables, the angles of incidence as input variables to the model. In this work, we use support vector regression (SVR) to compare this approach with a new strategy which consists in grouping the refletarray elements under a small set of angles of incidence and train surrogate models per angle of incidence pair. In this case, the dimensionality of the SVR decreases in two with regard to the former approach. In both cases, two geometrical variables are considered for reflectarray design, obtaining 4-D and 2-D SVRs, respectively. In contrast to the common approach in the literature, the comparison between the 4-D and 2-D SVRs shows that a proper discretization of the angles of incidence is more competitive than introducing the angles as input variables in the SVR. The 2-D SVR offers a shorter training time, faster reflectarray analysis, and a similar accuracy than the 4-D SVR, making it more suitable for design and optimization procedures.
- Angle of incidence
- machine learning
- reflectarray antenna
- support vector regression (SVR)
- surrogate model
ASJC Scopus subject areas
- Electrical and Electronic Engineering