Consensus Deep Neural Networks for Antenna Design and Optimization

We present a general approach for antenna design and optimization based on consensus of results from a number of independently trained deep neural networks (DNNs). The aim of using the consensus is to reduce the uncertainty of results from a single DNN. The approach leads to several orders of magnitude faster antenna optimization and design compared to the optimization based on a full-wave solver and allows a compromise between the analysis speed and its accuracy. The used DNNs are multilayer perceptrons (MLP) with multiple fully connected hidden layers. As an example, we consider the Yagi-Uda antenna with four design parameters and optimize it for the maximal forward gain. The training of neural networks is done on datasets of several sizes, up to 1 million antenna samples. The samples are generated either randomly or at a uniform grid over the design space using the method of moments.