Antenna Design and Optimization Using Machine Learning

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.


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Dr. C.J. Reddy
Vice President, Business Development - Electromagnetics
Dr. Reddy was awarded the US National Research Council (NRC) Resident Research Associateship at NASA Langley Research Center. He is currently a Fellow of IEEE, ACES and AMTA and has published 37 journal papers, 77 conference papers and 18 NASA Technical Reports to date.
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Gopinath Gampala
Technical Regional Manager
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.