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Machine Learning for Exotic Metasurfaces

Publication ,  Conference
Deng, Y; Ren, S; Fan, K; Malof, JM; Padilla, WJ
Published in: International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz
November 8, 2020

We introduce one deep neural network and an inverse method called neural adjoint method to solve high-dimensional metasurface inverse design problem. We find that, even when the solution is outside the geometry space, the neural adjoint method is still able to find a metasurface design with low error.

Duke Scholars

Published In

International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz

DOI

EISSN

2162-2035

ISSN

2162-2027

Publication Date

November 8, 2020

Volume

2020-November

Start / End Page

25
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Deng, Y., Ren, S., Fan, K., Malof, J. M., & Padilla, W. J. (2020). Machine Learning for Exotic Metasurfaces. In International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz (Vol. 2020-November, p. 25). https://doi.org/10.1109/IRMMW-THz46771.2020.9370973
Deng, Y., S. Ren, K. Fan, J. M. Malof, and W. J. Padilla. “Machine Learning for Exotic Metasurfaces.” In International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz, 2020-November:25, 2020. https://doi.org/10.1109/IRMMW-THz46771.2020.9370973.
Deng Y, Ren S, Fan K, Malof JM, Padilla WJ. Machine Learning for Exotic Metasurfaces. In: International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz. 2020. p. 25.
Deng, Y., et al. “Machine Learning for Exotic Metasurfaces.” International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz, vol. 2020-November, 2020, p. 25. Scopus, doi:10.1109/IRMMW-THz46771.2020.9370973.
Deng Y, Ren S, Fan K, Malof JM, Padilla WJ. Machine Learning for Exotic Metasurfaces. International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz. 2020. p. 25.

Published In

International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz

DOI

EISSN

2162-2035

ISSN

2162-2027

Publication Date

November 8, 2020

Volume

2020-November

Start / End Page

25