Multipolar Resonance Engineering Using Machine Learning
Publication
, Conference
Li, W; Sedeh, HB; Padilla, WJ; Malof, J; Litchinitser, NM
Published in: International Conference on Metamaterials, Photonic Crystals and Plasmonics
January 1, 2023
We developed machine learning models to predict the multipolar resonances and electric field distributions of all-dielectric meta-atoms. Machine learning method is also used for inverse designing meta-atoms based on the desired multipolar resonances.
Duke Scholars
Published In
International Conference on Metamaterials, Photonic Crystals and Plasmonics
EISSN
2429-1390
Publication Date
January 1, 2023
Start / End Page
1175
Citation
APA
Chicago
ICMJE
MLA
NLM
Li, W., Sedeh, H. B., Padilla, W. J., Malof, J., & Litchinitser, N. M. (2023). Multipolar Resonance Engineering Using Machine Learning. In International Conference on Metamaterials, Photonic Crystals and Plasmonics (p. 1175).
Li, W., H. B. Sedeh, W. J. Padilla, J. Malof, and N. M. Litchinitser. “Multipolar Resonance Engineering Using Machine Learning.” In International Conference on Metamaterials, Photonic Crystals and Plasmonics, 1175, 2023.
Li W, Sedeh HB, Padilla WJ, Malof J, Litchinitser NM. Multipolar Resonance Engineering Using Machine Learning. In: International Conference on Metamaterials, Photonic Crystals and Plasmonics. 2023. p. 1175.
Li, W., et al. “Multipolar Resonance Engineering Using Machine Learning.” International Conference on Metamaterials, Photonic Crystals and Plasmonics, 2023, p. 1175.
Li W, Sedeh HB, Padilla WJ, Malof J, Litchinitser NM. Multipolar Resonance Engineering Using Machine Learning. International Conference on Metamaterials, Photonic Crystals and Plasmonics. 2023. p. 1175.
Published In
International Conference on Metamaterials, Photonic Crystals and Plasmonics
EISSN
2429-1390
Publication Date
January 1, 2023
Start / End Page
1175