Transfer Learning for Modeling Plasmonic Nanowire Waveguides

Transfer Learning for Modeling Plasmonic Nanowire Waveguides

Retrieving waveguiding properties of plasmonic metal nanowires (MNWs) through numerical simulations is time- and computational-resource-consuming, especially for those with abrupt geometric features and broken symmetries. Deep learning provides an alternative approach but is challenging to use due t...

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Bibliographic Details
Main Authors: Aoning Luo, Yuanjia Feng, Chunyan Zhu, Yipei Wang, Xiaoqin Wu
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Nanomaterials
Subjects:
deep learning
transfer learning
plasmonics
nanowires
waveguides
Online Access:https://www.mdpi.com/2079-4991/12/20/3624

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