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    We developed a physics-informed neural network to characterize light transmission through scattering media. This method improves focusing efficiency and noise robustness for applications in optics and imaging.

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    Area of Science:

    • Optics and Photonics
    • Computational Physics
    • Machine Learning Applications

    Background:

    • Characterizing light transmission through complex scattering media is crucial for optical control.
    • Existing methods like phase-stepping holography face challenges such as output-phase ambiguity and dark spots.
    • Accurate characterization is essential for applications in optical networks, biomedical imaging, and quantum information processing.

    Purpose of the Study:

    • To present a novel method for characterizing transmission matrices of complex scattering media.
    • To overcome limitations of existing techniques, including the need for a reference field.
    • To demonstrate enhanced focusing efficiency and noise robustness for light control applications.

    Main Methods:

    • Utilizing a physics-informed, multi-plane neural network (MPNN) for transmission matrix characterization.
    • Measuring the transmission matrix of a commercial multi-mode fiber without requiring a known optical reference field.
    • Demonstrating the generalization of the method to characterize cascaded transmission matrices.

    Main Results:

    • Achieved accurate measurement of transmission matrices for complex scattering media.
    • Reported up to 58% improvement in focusing efficiency compared to phase-stepping holography.
    • Showcased significantly higher noise robustness than phase-stepping holography.

    Conclusions:

    • The developed MPNN method offers an essential tool for precise light control through complex media.
    • The technique effectively addresses output-phase ambiguity and dark spot issues.
    • The method's applicability extends to cascaded scattering media, enabling advanced light propagation control.