Multipolar Resonance Engineering Using Machine Learning

Wenhao Li; Hooman Barati Sedeh; Willie J. Padilla; Jordan Malof; Natalia M. Litchinitser

Multipolar Resonance Engineering Using Machine Learning

Wenhao Li
, Hooman Barati Sedeh
, Willie J. Padilla
, Jordan Malof
, Natalia M. Litchinitser

Computer Science

Duke University

Research output: Contribution to journal › Conference article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	1175
Number of pages	1
Journal	International Conference on Metamaterials, Photonic Crystals and Plasmonics
State	Published - 2023
Event	13th International Conference on Metamaterials, Photonic Crystals and Plasmonics, META 2023 - Paris, France Duration: Jul 18 2023 → Jul 21 2023

Cite this

@article{6fd3cc70aa75450992a358e92193faf6,

title = "Multipolar Resonance Engineering Using Machine Learning",

abstract = "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.",

author = "Wenhao Li and Sedeh, \{Hooman Barati\} and Padilla, \{Willie J.\} and Jordan Malof and Litchinitser, \{Natalia M.\}",

note = "Publisher Copyright: {\textcopyright} 2023, META Conference. All rights reserved.; 13th International Conference on Metamaterials, Photonic Crystals and Plasmonics, META 2023 ; Conference date: 18-07-2023 Through 21-07-2023",

year = "2023",

language = "English",

pages = "1175",

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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.

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 - https://www.scopus.com/pages/publications/85174639283

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 -

Multipolar Resonance Engineering Using Machine Learning

Abstract

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