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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Detalles Bibliográficos
Autores principales: Luo, Aoning, Feng, Yuanjia, Zhu, Chunyan, Wang, Yipei, Wu, Xiaoqin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9612048/
https://www.ncbi.nlm.nih.gov/pubmed/36296814
http://dx.doi.org/10.3390/nano12203624

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9612048/
https://www.ncbi.nlm.nih.gov/pubmed/36296814
http://dx.doi.org/10.3390/nano12203624

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