Deep Learning Approach for Directivity Prediction of Multi-Layered Leaky Wave Antenna

This work provides a solution for the forward design problem of leaky wave antennas to predict its directivity using a novel deep neural network architecture. Considering the nonlinear relation of the angle of maximum directivity and frequency at leaky wave resonance, this paper leverages the advantage of deep learning framework for such non-linear regression problem. Using the Maxwell's equations, the dataset has been generated for different permittivity and thickness values of three-layered leaky wave antenna. For training purpose, it considers 80% of total data. Remaining 20% data are used to obtain an accurate prediction of the maximum directivity within less than 1% error. The proposed method can be suitable replacement for rigorous full-wave simulation in terms of time and computational resource.