Machine Learning Regression Techniques for the Modeling of Complex Systems: An Overview

Abstract

Recently, machine learning (ML) techniques have gained widespread diffusion, since they have been successfully applied in several research fields. This paper investigates the effectiveness of advanced ML regressions in two EMC applications. Specifically, support vector machine, least-squares support vector machine and Gaussian process regressions are adopted to construct accurate and fast-to-evaluate surrogate models able to predict the output variable of interest as a function of the system parameters. The resulting surrogates, built from a limited set of training samples, can be suitably adopted for both uncertainty quantification and optimization purposes. The accuracy and the key features of each of the considered machine learning techniques are investigated by comparing their predictions with the ones provided by either circuital simulations or measurements.