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dc.contributor.authorAhmed, Waqas Waseem
dc.contributor.authorFarhat, Mohamed
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorWu, Ying
dc.date.accessioned2021-02-14T08:53:26Z
dc.date.available2020-11-03T12:52:48Z
dc.date.available2021-02-14T08:53:26Z
dc.date.issued2021-02-12
dc.date.submitted2020-11-23
dc.identifier.citationAhmed, W. W., Farhat, M., Zhang, X., & Wu, Y. (2021). Deterministic and probabilistic deep learning models for inverse design of broadband acoustic cloak. Physical Review Research, 3(1). doi:10.1103/physrevresearch.3.013142
dc.identifier.issn2643-1564
dc.identifier.doi10.1103/physrevresearch.3.013142
dc.identifier.urihttp://hdl.handle.net/10754/665791
dc.description.abstractConcealing an object from incoming waves (light and/or sound) remained science fiction for a long time due to the absence of wave-shielding materials in nature. Yet, the invention of artificial materials and new physical principles for optical and sound wave manipulation translated this abstract concept into reality by making an object optically and acoustically “invisible.” Here, we present the notion of a machine learning driven acoustic cloak and demonstrate an example of such a cloak with a multilayered core-shell configuration. We develop deterministic and probabilistic deep learning models based on autoencoderlike neural network structure to retrieve the structural and material properties of the cloaking shell surrounding the object that suppresses scattering of sound in a broad spectral range, as if it was not there. The probabilistic model enhances the generalization ability of design procedure and uncovers the sensitivity of the cloak’s parameters on the spectral response for practical implementation. This proposal opens up avenues to expedite the design of intelligent cloaking devices for tailored spectral response and offers a feasible solution for inverse scattering problems.
dc.description.sponsorshipThe work described 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.
dc.publisherAmerican Physical Society (APS)
dc.relation.urlhttps://link.aps.org/doi/10.1103/PhysRevResearch.3.013142
dc.rightsPublished by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleDeterministic and probabilistic deep learning models for inverse design of broadband acoustic cloak
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.identifier.journalPhysical Review Research
dc.eprint.versionPublisher's Version/PDF
dc.identifier.volume3
dc.identifier.issue1
dc.identifier.arxivid2010.14866
kaust.personAhmed, Waqas Waseem
kaust.personFarhat, Mohamed
kaust.personZhang, Xiangliang
kaust.personWu, Ying
kaust.grant.numberBAS/1/1626-01-01
dc.date.accepted2021-01-22
refterms.dateFOA2020-11-03T12:53:19Z
dc.date.posted2020-10-28


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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.
Except where otherwise noted, this item's license is described as Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.
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