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Multifrequency spherical cloak in microwave frequencies enabled by deep learning.

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    Deep learning efficiently designs invisibility cloaks by predicting scattering cross section and optimizing structural parameters. This data-driven approach achieves perfect invisibility for multiple wave types and frequencies, revolutionizing cloak design.

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    Area of Science:

    • Electromagnetics and Metamaterials
    • Computational Physics
    • Applied Artificial Intelligence

    Background:

    • Designing invisibility devices is crucial for anti-detection applications in communications and construction.
    • Traditional methods for designing cloaking devices are inefficient, relying on manual parameter tuning and trial-and-error.
    • The emergence of data-driven approaches, particularly deep learning, offers a promising alternative for complex design problems.

    Purpose of the Study:

    • To demonstrate the efficacy of a trained deep neural network for designing invisibility devices.
    • To accurately predict the scattering cross section (SCS) of multilayer spheres.
    • To reversely design structural parameters for achieving target spectral invisibility performance.

    Main Methods:

    • Development and training of a deep neural network (DNN) model.
    • DNN prediction of scattering cross section (SCS) based on structural parameters.
    • Inverse design using the DNN to determine structural parameters for desired invisibility.
    • Validation through three-dimensional full-wave simulations.

    Main Results:

    • The DNN accurately predicts the scattering cross section (SCS) for multilayer spheres.
    • The inverse design capability successfully identifies structural parameters for target spectra.
    • Simulations confirm perfect invisibility performance under transverse electric (TE) and transverse magnetic (TM) waves.
    • Invisibility is achieved across multiple frequencies and for point source illumination.

    Conclusions:

    • Deep learning provides an efficient and accurate method for designing invisibility devices.
    • The data-driven approach significantly reduces the time and effort compared to traditional methods.
    • This work highlights the potential of deep learning to revolutionize electromagnetic cloaking design.