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    Machine learning for knowledge acquisition and accelerated inverse-design for non-Hermitian systems

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    2204.13376.pdf
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    Type
    Preprint
    Authors
    Ahmed, Waqas Waseem
    Farhat, Mohamed
    Staliunas, K.
    Zhang, X.
    Wu, Y.
    KAUST Department
    Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    KAUST Grant Number
    BAS/1/1626-01-01
    Date
    2022-04-28
    Permanent link to this record
    http://hdl.handle.net/10754/676744
    
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    Abstract
    Non-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 proposes 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 pave the way for intelligent inverse design and shape our understanding of the physical mechanism in general non-Hermitian systems.
    Sponsors
    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).
    Publisher
    arXiv
    arXiv
    2204.13376
    Additional Links
    https://arxiv.org/pdf/2204.13376.pdf
    Collections
    Preprints; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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