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Physics-informed neural networks for inverse problems in nano-optics and metamaterials

Chen, Yuyao ; Lu, Lu ; Karniadakis, George Em ; Dal Negro, Luca

Optics express, 2020-04, Vol.28 (8), p.11618-11633 [Periódico revisado por pares]

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Título:
Physics-informed neural networks for inverse problems in nano-optics and metamaterials
Autor: Chen, Yuyao ; Lu, Lu ; Karniadakis, George Em ; Dal Negro, Luca
Assuntos: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS ; Optics
É parte de: Optics express, 2020-04, Vol.28 (8), p.11618-11633
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
PhILMs project (No. de-sc0019453); SC0019453
USDOE Office of Science (SC)
Descrição: In this paper, we employ the emerging paradigm of physics-informed neural networks (PINNs) for the solution of representative inverse scattering problems in photonic metamaterials and nano-optics technologies. In particular, we successfully apply mesh-free PINNs to the difficult task of retrieving the effective permittivity parameters of a number of finite-size scattering systems that involve many interacting nanostructures as well as multi-component nanoparticles. Our methodology is fully validated by numerical simulations based on the finite element method (FEM). The development of physics-informed deep learning techniques for inverse scattering can enable the design of novel functional nanostructures and significantly broaden the design space of metamaterials by naturally accounting for radiation and finite-size effects beyond the limitations of traditional effective medium theories.
Editor: United States: Optical Society of America
Idioma: Inglês

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