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References and Resources

T. Asano and S. Noda. “Optimization of photonic crystal nanocavities based on deep learning,” Opt. Express 26, 32704 (2018).

J. Peurifoy et al. “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4, eaar420 (2018).

M. Chen et al. “WaveY-Net: Physics-augmented deep learning for high-speed electromagnetic simulation and optimization,” arXiv:1710.10196v3 [cs.NE] (2018).

H.-A. Joung et al. “Point-of-Care Serodiagnostic Test for Early-Stage Lyme Disease Using a Multiplexed Paper-Based Immunoassay and Machine Learning,” ACS Nano 14, 229 (2020).

P.R. Wiecha and O.L. Muskens. “Deep Learning Meets Nanophotonics: A Generalized Accurate Predictor for Near Fields and Far Fields of Arbitrary 3D Nanostructures,” Nano Lett. 20, 329 (2020).

A.-P. Blanchard-Dionne and O.J.F. Martin “Successive training of a generative adversarial network for the design of an optical cloak,” OSA Continuum 4, 87 (2021).

J. Jiang et al. “Deep neural networks for the evaluation and design of photonic devices,” Nat. Rev. Mater. 6, 679 (2021).

W. Ma et al. “Pushing the Limits of Functionality-Multiplexing Capability in Metasurface Design Based on Statistical Machine Learning,” Adv. Mater. 34, 2110022 (2022).

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