Machine learning for knowledge acquisition and accelerated inverse-design for non-Hermitian systems

Non-Hermitian systems offer new platforms for unusual physical properties that can be flexibly manipulated by redistribution of the real and imaginary parts of refractive indices, whose presence breaks conventional wave propagation symmetries, leading to asymmetric reflection and symmetric transmiss...

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Detalhes bibliográficos
Autores: Ahmed, Waqas W., Farhat, Mohamed, Staliunas, Kestutis|||0000-0002-0539-9538, Zhang, Xiangliang, Wu, Ying
Formato: artículo
Fecha de publicación:2023
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/380358
Acesso em linha:https://hdl.handle.net/2117/380358
https://dx.doi.org/10.1038/s42005-022-01121-9
Access Level:acceso abierto
Palavra-chave:Plasmons (Physics)
Nanophotonics
Micro-optics
Nanophotonics and plasmonics
Plasmons (Física)
Nanofotònica
Àrees temàtiques de la UPC::Física
Descrição
Resumo:Non-Hermitian systems offer new platforms for unusual physical properties that can be flexibly manipulated by redistribution of the real and imaginary parts of refractive indices, whose presence breaks conventional wave propagation symmetries, leading to asymmetric reflection and symmetric transmission with respect to the wave propagation direction. Here, we use supervised and unsupervised learning techniques for knowledge acquisition in non-Hermitian systems which accelerate the inverse design process. In particular, we construct a deep learning model that relates the transmission and asymmetric reflection in non-conservative settings and propose sub-manifold learning to recognize non-Hermitian features from transmission spectra. The developed deep learning framework determines the feasibility of a desired spectral response for a given structure and uncovers the role of effective gain-loss parameters to tailor the spectral response. These findings offer a route for intelligent inverse design and contribute to the understanding of physical mechanism in general non-Hermitian systems.