Deep learning models for acoustic scattering problems

Waqas Waseem Ahmed; Mohamed Farhat; Pai Yen Chen; Xiangliang Zhang; Ying Wu

Deep learning models for acoustic scattering problems

Waqas Waseem Ahmed, Mohamed Farhat, Pai Yen Chen, Xiangliang Zhang, Ying Wu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We develop deep learning (DL) models based on discriminative and generative networks to solve the forward and inverse acoustic scattering problems and show how these models streamlines the inverse design process by eliminating the degenerate solution space. Specifically, we present DL frameworks for designing broadband acoustic cloaks and arbitrarily-shape acoustic object recognition for underwater applications.

Original language	English (US)
Title of host publication	24th International Congress on Acoustics, ICA 2022
Publisher	International Commission for Acoustics (ICA)
State	Published - Jan 1 2022

Bibliographical note

KAUST Repository Item: Exported on 2023-05-24
Acknowledged KAUST grant number(s): BAS/1/1626-01-01
Acknowledgements: The work described in here is supported by King Abdullah University of Science and Technology (KAUST) Artificial Intelligence Initiative Fund and KAUST Baseline Research Fund No. BAS/1/1626-01-01.

Cite this

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title = "Deep learning models for acoustic scattering problems",

abstract = "We develop deep learning (DL) models based on discriminative and generative networks to solve the forward and inverse acoustic scattering problems and show how these models streamlines the inverse design process by eliminating the degenerate solution space. Specifically, we present DL frameworks for designing broadband acoustic cloaks and arbitrarily-shape acoustic object recognition for underwater applications.",

author = "Ahmed, {Waqas Waseem} and Mohamed Farhat and Chen, {Pai Yen} and Xiangliang Zhang and Ying Wu",

note = "KAUST Repository Item: Exported on 2023-05-24 Acknowledged KAUST grant number(s): BAS/1/1626-01-01 Acknowledgements: The work described in here is supported by King Abdullah University of Science and Technology (KAUST) Artificial Intelligence Initiative Fund and KAUST Baseline Research Fund No. BAS/1/1626-01-01.",

year = "2022",

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language = "English (US)",

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TY - GEN

T1 - Deep learning models for acoustic scattering problems

AU - Ahmed, Waqas Waseem

AU - Farhat, Mohamed

AU - Chen, Pai Yen

AU - Zhang, Xiangliang

AU - Wu, Ying

N1 - KAUST Repository Item: Exported on 2023-05-24 Acknowledged KAUST grant number(s): BAS/1/1626-01-01 Acknowledgements: The work described in here is supported by King Abdullah University of Science and Technology (KAUST) Artificial Intelligence Initiative Fund and KAUST Baseline Research Fund No. BAS/1/1626-01-01.

PY - 2022/1/1

Y1 - 2022/1/1

N2 - We develop deep learning (DL) models based on discriminative and generative networks to solve the forward and inverse acoustic scattering problems and show how these models streamlines the inverse design process by eliminating the degenerate solution space. Specifically, we present DL frameworks for designing broadband acoustic cloaks and arbitrarily-shape acoustic object recognition for underwater applications.

AB - We develop deep learning (DL) models based on discriminative and generative networks to solve the forward and inverse acoustic scattering problems and show how these models streamlines the inverse design process by eliminating the degenerate solution space. Specifically, we present DL frameworks for designing broadband acoustic cloaks and arbitrarily-shape acoustic object recognition for underwater applications.

UR - http://hdl.handle.net/10754/691966

UR - https://ica2022korea.org/data/ICA2022_Program.pdf

UR - http://www.scopus.com/inward/record.url?scp=85153776672&partnerID=8YFLogxK

M3 - Conference contribution

BT - 24th International Congress on Acoustics, ICA 2022

PB - International Commission for Acoustics (ICA)

ER -

Deep learning models for acoustic scattering problems

Abstract

Bibliographical note

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