Comparative analysis of machine learning techniques for metamaterial absorber performance in terahertz applications

Metamaterial absorbers (MMAs) have tremendous potential for controlling and modulating Terahertz electromagnetic (EM) waves. It is challenging to design MMAs for optimal performance using conventional methods due to their time-consuming and computationally demanding nature. Machine learning (ML) reg...

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Bibliographic Details

Published in:	Alexandria Engineering Journal
Main Authors:	Prince Jain, Mohammad Tariqul Islam, Ahmed S. Alshammari
Format:	Article
Language:	English
Published:	Elsevier 2024-09-01
Subjects:	Bagging Extra trees K-nearest neighbors Metamaterial absorber Machine learning Random forest
Online Access:	http://www.sciencedirect.com/science/article/pii/S1110016824005799