Inverse design of reflective metasurface antennas using deep learning from small‐scale statistically random pico‐cells

A small‐scale data‐selection method is proposed for deep‐learning‐based inverse design of reflective metasurface antennas (MAs). The overall design process involves discretization, coding, data selection, simulation, feature representation, training, prediction, and metasurface design. Unlike conven...

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Bibliographic Details

Published in:	Microwave and optical technology letters Vol. 66; no. 2
Main Authors:	You, Xi Chong, Lin, Feng Han
Format:	Journal Article
Language:	English
Published:	New York Wiley Subscription Services, Inc 01.02.2024
Subjects:	Antennas Boresights Coding deep convolutional variational autoencoder (DCVAE) Deep learning Discretization Inverse design Machine learning metasurface antennas (MAs) Metasurfaces Pencil beams Simulation statistically‐random data filtering (SRDF) Unit cell
ISSN:	0895-2477, 1098-2760
Online Access:	Get full text
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