TY - GEN
T1 - A Machine Learning-based Approach to Model Highly-thermally Robust Metasurface Absorber
AU - Ijaz, Sumbel
AU - Noureen, Sadia
AU - Rehman, Bacha
AU - Zubair, Muhammad
AU - Mehmood, Muhammad Qasim
AU - Massoud, Yehia Mahmoud
N1 - KAUST Repository Item: Exported on 2023-08-14
PY - 2022
Y1 - 2022
N2 - For accelerating design procedure of compact and efficient on-chip nano-photonics and to aid computationally expensive, time-exhaustive state-of-the-art iterative simulation schemes, regression-based machine-learning models are demonstrated that predict the optical response and structural parameters of the meta-atoms.
AB - For accelerating design procedure of compact and efficient on-chip nano-photonics and to aid computationally expensive, time-exhaustive state-of-the-art iterative simulation schemes, regression-based machine-learning models are demonstrated that predict the optical response and structural parameters of the meta-atoms.
UR - http://hdl.handle.net/10754/693546
UR - https://opg.optica.org/abstract.cfm?URI=CLEOPR-2022-CPDP_05
UR - http://www.scopus.com/inward/record.url?scp=85166473255&partnerID=8YFLogxK
U2 - 10.1364/CLEOPR.2022.CPDP_05
DO - 10.1364/CLEOPR.2022.CPDP_05
M3 - Conference contribution
BT - Proceedings of the 2022 Conference on Lasers and Electro-Optics Pacific Rim
PB - Optica Publishing Group
ER -