TY - GEN
T1 - Mie Resonance-based Meta-atom Design with Machine Learning Method
AU - Li, Wenhao
AU - Sedeh, Hooman Barati
AU - Padilla, Willie J.
AU - Malof, Jordan
AU - Litchinitser, Natalia M.
N1 - Publisher Copyright:
© 2023 The Author (s).
PY - 2023
Y1 - 2023
N2 - Forward prediction machine learning models were developed to predict the scattering behaviors and electromagnetic field of meta-atoms, while an inverse design model was built for reconstructing meta-atoms under the guidance of multipole expansion theory.
AB - Forward prediction machine learning models were developed to predict the scattering behaviors and electromagnetic field of meta-atoms, while an inverse design model was built for reconstructing meta-atoms under the guidance of multipole expansion theory.
UR - http://www.scopus.com/inward/record.url?scp=85191504220&partnerID=8YFLogxK
U2 - 10.1364/CLEO_AT.2023.SW4P.5
DO - 10.1364/CLEO_AT.2023.SW4P.5
M3 - Conference contribution
AN - SCOPUS:85191504220
T3 - CLEO: Science and Innovations, CLEO:S and I 2023
BT - CLEO
T2 - CLEO: Science and Innovations, CLEO:S and I 2023 - Part of Conference on Lasers and Electro-Optics 2023
Y2 - 7 May 2023 through 12 May 2023
ER -