On-Demand Short Course
Machine learning is a method of data analysis that automates analytical model building. As the complexity of antennas increases each day, antenna designers can take advantage of machine learning to generate trained models for their physical antenna designs and perform fast and intelligent optimization on these trained models. Using the trained models, different optimization algorithms and goals can be run quickly, in seconds, that can be utilized for comparison studies, stochastic analysis for tolerance studies etc.
This short course presents the process of fast and intelligent optimization by adopting the Design of Experiments (DOE) and Machine Learning using Altair FEKO. We discuss specific examples that showcase the advantages of using ML for antenna design and optimization.
Gopi graduated from University of Mississippi with a Master’s degree in computational electromagnetics in 2007 and working in the field of CAE since then. He is a member of IEEE and published extensively on topics like High-impedance surfaces, Low-profile antennas, LTE, Radomes, Characteristic Mode Analysis, 5G and Machine Learning.