Deep learning models for acoustic wave scatterings

W. W. Amhed; M. Farhat; P. Y. Chen; X. L. Zhang; Y. Wu

Deep learning models for acoustic wave scatterings

W. W. Amhed, M. Farhat, P. Y. Chen, X. L. Zhang, Y. Wu^*

^*Corresponding author for this work

Research output: Contribution to conference › Paper › peer-review

Abstract

We develop deep learning models based on discriminative and generative networks to solve the forward and inverse acoustic scattering problems and show how these models benefit solving the inverse design process by eliminating the non-unique solution space. We demonstrate examples of using the developed deep learning models for designing broadband acoustic cloaks and arbitrarily-shape acoustic object recognition for underwater applications.

Original language	English (US)
Pages	510-511
Number of pages	2
State	Published - 2023
Event	13th International Conference on Metamaterials, Photonic Crystals and Plasmonics, META 2023 - Paris, France Duration: Jul 18 2023 → Jul 21 2023

Conference

Conference	13th International Conference on Metamaterials, Photonic Crystals and Plasmonics, META 2023
Country/Territory	France
City	Paris
Period	07/18/23 → 07/21/23

ASJC Scopus subject areas

Electrical and Electronic Engineering
Materials Science (miscellaneous)
Electronic, Optical and Magnetic Materials
Materials Chemistry

Cite this

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title = "Deep learning models for acoustic wave scatterings",

abstract = "We develop deep learning models based on discriminative and generative networks to solve the forward and inverse acoustic scattering problems and show how these models benefit solving the inverse design process by eliminating the non-unique solution space. We demonstrate examples of using the developed deep learning models for designing broadband acoustic cloaks and arbitrarily-shape acoustic object recognition for underwater applications.",

author = "Amhed, {W. W.} and M. Farhat and Chen, {P. Y.} and Zhang, {X. L.} and Y. Wu",

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 (US)",

pages = "510--511",

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TY - CONF

T1 - Deep learning models for acoustic wave scatterings

AU - Amhed, W. W.

AU - Farhat, M.

AU - Chen, P. Y.

AU - Zhang, X. L.

AU - Wu, Y.

PY - 2023

Y1 - 2023

N2 - We develop deep learning models based on discriminative and generative networks to solve the forward and inverse acoustic scattering problems and show how these models benefit solving the inverse design process by eliminating the non-unique solution space. We demonstrate examples of using the developed deep learning models for designing broadband acoustic cloaks and arbitrarily-shape acoustic object recognition for underwater applications.

AB - We develop deep learning models based on discriminative and generative networks to solve the forward and inverse acoustic scattering problems and show how these models benefit solving the inverse design process by eliminating the non-unique solution space. We demonstrate examples of using the developed deep learning models for designing broadband acoustic cloaks and arbitrarily-shape acoustic object recognition for underwater applications.

UR - http://www.scopus.com/inward/record.url?scp=85174601231&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:85174601231

SP - 510

EP - 511

T2 - 13th International Conference on Metamaterials, Photonic Crystals and Plasmonics, META 2023

Y2 - 18 July 2023 through 21 July 2023

ER -

Deep learning models for acoustic wave scatterings

Abstract

Conference

ASJC Scopus subject areas

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