Machine Learning Assisted Hybrid Electromagnetic Modeling Framework and Its Applications to UWB MIMO Antennas
Debanjali Sarkar, Taimoor Khan, Jayadeva, Ahmed A. Kishk
Abstract
Machine learning (ML) has gained recognition as an efficient and robust technique to realize the solution of electromagnetic forward and inverse problems. This article introduces a hybrid ML framework that simultaneously acts as a forward and inverse model based on a mode input. Multivariate relevance vector regression (MVRVR) is adopted for implementing the hybrid ML model. MVRVR models for forward and inverse modeling are also presented. In addition, three hybrid ML models based on support vector regression (SVR), Gaussian process regression (GPR), and artificial neural network (ANN) are also implemented and a thorough comparative analysis between these ML models with the proposed MVRVR model is investigated to verify its accuracy. The proposed hybrid framework can be used to replace the requirements of the two separate models for solving forward and inverse problems. Two examples of ultra-wideband (UWB) MIMO antennas are employed to validate the effectiveness of the proposed modeling framework.