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 proceedingConference contribution


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 languageEnglish (US)
Title of host publication24th International Congress on Acoustics, ICA 2022
PublisherInternational Commission for Acoustics (ICA)
StatePublished - 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.


Dive into the research topics of 'Deep learning models for acoustic scattering problems'. Together they form a unique fingerprint.

Cite this