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Cited by 450

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Deep learning for accelerated all-dielectric metasurface design

Open access
Nadell, Christian C., Huang, Bohao, Malof, Jordan M., Padilla, Willie J.·2019·Optics Express

Abstract

and is over five orders of magnitude faster than conventional electromagnetic simulation software. We further develop a novel method to solve the inverse modeling problem, termed fast forward dictionary search (FFDS), which offers tremendous controls to the designer and only requires an accurate forward neural network model. These techniques significantly increase the viability of more complex all-dielectric metasurface designs and provide opportunities for the future of tailored light matter interactions.

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Metadata

Authors
Nadell, C. C., Huang, B., Malof, J. M., & Padilla, W. J.
Venue
Optics Express
Published
2019-09-16
Volume
27
Issue
20
Pages
27523
ISSN
1094-4087 (ISSN-L)
OpenAlex
W2974878544
Cited by
450
References
6
FWCI
15.72
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Deep learning for accelerated all-dielectric metasurface design. https://app.cochat.ai/papers/10.1364/oe.27.027523
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Nadell, C. C., Huang, B., Malof, J. M., & Padilla, W. J. (2019). Deep learning for accelerated all-dielectric metasurface design. Optics Express. https://doi.org/10.1364/oe.27.027523

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.