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dc.contributor.authorAhmed, Waqas Waseem
dc.contributor.authorFarhat, Mohamed
dc.contributor.authorStaliunas, Kestutis
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorWu, Ying
dc.date.accessioned2023-01-04T07:43:12Z
dc.date.available2022-05-11T08:12:25Z
dc.date.available2023-01-04T07:43:12Z
dc.date.issued2023-01-03
dc.identifier.citationAhmed, W. W., Farhat, M., Staliunas, K., Zhang, X., & Wu, Y. (2023). Machine learning for knowledge acquisition and accelerated inverse-design for non-Hermitian systems. Communications Physics, 6(1). https://doi.org/10.1038/s42005-022-01121-9
dc.identifier.issn2399-3650
dc.identifier.doi10.1038/s42005-022-01121-9
dc.identifier.urihttp://hdl.handle.net/10754/676744
dc.description.abstractNon-Hermitian systems offer new platforms for unusual physical properties that can be flexibly manipulated by redistribution of the real and imaginary parts of refractive indices, whose presence breaks conventional wave propagation symmetries, leading to asymmetric reflection and symmetric transmission with respect to the wave propagation direction. Here, we use supervised and unsupervised learning techniques for knowledge acquisition in non-Hermitian systems which accelerate the inverse design process. In particular, we construct a deep learning model that relates the transmission and asymmetric reflection in non-conservative settings and propose sub-manifold learning to recognize non-Hermitian features from transmission spectra. The developed deep learning framework determines the feasibility of a desired spectral response for a given structure and uncovers the role of effective gain-loss parameters to tailor the spectral response. These findings offer a route for intelligent inverse design and contribute to the understanding of physical mechanism in general non-Hermitian systems.
dc.description.sponsorshipThe 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. K.S. acknowledges funding from European Social Fund (project No 09.3.3-LMT-K712-17- 0016) under grant agreement with the Research Council of Lithuania (LMTLT), and from the Spanish Ministerio de Ciencia e Innovación under grant No.385 (PID2019-109175GB-C21).
dc.publisherSpringer Science and Business Media LLC
dc.relation.urlhttps://www.nature.com/articles/s42005-022-01121-9
dc.rightsArchived with thanks to Communications Physics under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleMachine learning for knowledge acquisition and accelerated inverse-design for non-Hermitian systems
dc.typeArticle
dc.contributor.departmentDivision of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalCommunications Physics
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA
dc.identifier.volume6
dc.identifier.issue1
dc.identifier.arxivid2204.13376
kaust.personAhmed, Waqas Waseem
kaust.personFarhat, Mohamed
kaust.personZhang, Xiangliang
kaust.personWu, Ying
kaust.grant.numberBAS/1/1626-01-01
dc.date.accepted2022-12-16
refterms.dateFOA2022-05-11T08:13:47Z
kaust.acknowledged.supportUnitArtificial Intelligence Initiative Fund
kaust.acknowledged.supportUnitBaseline Research Fund


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Archived with thanks to Communications Physics under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0
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