A Machine Learning-based Approach to Model Highly-thermally Robust Metasurface Absorber

Sumbel Ijaz; Sadia Noureen; Bacha Rehman; Muhammad Zubair; Muhammad Qasim Mehmood; Yehia Massoud

A Machine Learning-based Approach to Model Highly-thermally Robust Metasurface Absorber

Sumbel Ijaz
, Sadia Noureen
, Bacha Rehman
, Muhammad Zubair
, Muhammad Qasim Mehmood
, Yehia Massoud

Science and Engineering

Research output: Chapter in Book/Report/Published conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 2022 Conference on Lasers and Electro-Optics Pacific Rim
Publisher	Optica Publishing Group
Publication status	Published - 2022

Access to Document

https://opg.optica.org/abstract.cfm?URI=CLEOPR-2022-CPDP_05

Cite this

@inproceedings{a0aa75bca81d4221bc8f541ab1e66766,

title = "A Machine Learning-based Approach to Model Highly-thermally Robust Metasurface Absorber",

abstract = "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.",

author = "Sumbel Ijaz and Sadia Noureen and Bacha Rehman and Muhammad Zubair and Mehmood, \{Muhammad Qasim\} and Yehia Massoud",

year = "2022",

language = "English",

booktitle = "Proceedings of the 2022 Conference on Lasers and Electro-Optics Pacific Rim",

publisher = "Optica Publishing Group",

}

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

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.

M3 - Conference contribution

BT - Proceedings of the 2022 Conference on Lasers and Electro-Optics Pacific Rim

PB - Optica Publishing Group

ER -