Using Machine Learning to Represent Electromagnetic Characteristics of Arbitrarily-shaped Targets

Abstract

A general data sparse representation of electromagnetic characteristics of an arbitrarily-shaped target is developed by using the machine learning model. The data sparse representation of the electromagnetic response is firstly figured out by the skeletonization technique. The machine learning approach is then employed to construct a general and flexible model which can capture the electromagnetic characteristics of the target of interest. Numerical experiments are conducted to validate the performance of the model.