TY - JOUR
T1 - Multipolar Resonance Engineering Using Machine Learning
AU - Li, Wenhao
AU - Sedeh, Hooman Barati
AU - Padilla, Willie J.
AU - Malof, Jordan
AU - Litchinitser, Natalia M.
N1 - Publisher Copyright:
© 2023, META Conference. All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85174639283&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85174639283
SP - 1175
JO - International Conference on Metamaterials, Photonic Crystals and Plasmonics
JF - International Conference on Metamaterials, Photonic Crystals and Plasmonics
T2 - 13th International Conference on Metamaterials, Photonic Crystals and Plasmonics, META 2023
Y2 - 18 July 2023 through 21 July 2023
ER -