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Machine Learning: TCO‐Based Active Dielectric Metasurfaces Design by Conditional Generative Adversarial Networks (Adv. Theory Simul. 2/2021)
Author(s) -
JafarZanjani Samad,
Salary Mohammad Mahdi,
Huynh Dat,
Elhamifar Ehsan,
Mosallaei Hossein
Publication year - 2021
Publication title -
advanced theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.068
H-Index - 17
ISSN - 2513-0390
DOI - 10.1002/adts.202170003
Subject(s) - generative grammar , inverse , adversarial system , computer science , modal , generative design , artificial intelligence , generative adversarial network , cluster analysis , machine learning , deep learning , engineering , materials science , mathematics , metric (unit) , operations management , geometry , polymer chemistry
In article 2000196, Hossein Mosallaei and co‐workers propose a machine learning (ML) method for broad‐band multi‐modal inverse design of TCO‐based active metasurfaces. Their proposed technique is a combination of a K‐means clustering algorithm and conditional generative adversarial networks (cGANs). It casts light on how ML models can solve inverse‐design problems and the level of intelligence they can provide.