Far-field Surrogate Model of Flexible Antennas Based on Vector Spherical Harmonics and Neural Network

To speed up the stochastic modeling of the far-field (FF) electric field of flexible antennas, a novel method combining vector spherical harmonics (VSH) and neural network (NN) is proposed to construct efficient and effective surrogate models. First, we use VSH to parsimoniously representing the antenna’s FF electric field vector with a limited number of modes; then, we use NN to map between the input variables and the VSH mode coefficients. We proposed an improved successive halving (ISH) algorithm to optimize the selection of hyperparameters when constructing the NN model. The results show that compared with the polynomial chaos expansion (PCE) model, the prediction error of the NN model has been reduced by 39.22% at the same modeling cost.