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Data-driven mirror system optimization design and digital twin.

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    A new model enhances mirror system optimization and real-time monitoring. This approach achieves accurate, diverse Pareto solutions and fast, visualized performance data for optomechanical systems.

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    Area of Science:

    • Optomechanics
    • Computational Engineering
    • Systems Engineering

    Background:

    • Increasing complexity in operating conditions and performance demands drive the need for enhanced precision in mirror systems.
    • This complexity complicates structural optimization and real-time performance monitoring.
    • Developing advanced methods for high-dimensional parametric modeling is crucial.

    Purpose of the Study:

    • To develop a high-dimensional parametric model for fast, multi-parameter, multi-constraint, and multi-objective optimization of mirror systems.
    • To create a digital twin platform for real-time online monitoring and performance visualization.
    • To enable fast wavefront reconstruction and real-time full-field deformation visualization.

    Main Methods:

    • Developed a high-dimensional parametric model for mirror systems.
    • Employed the constraint surrogate assisted evolutionary algorithm CNSGAIII-EHVI for collaborative optimization.
    • Integrated a Radial Basis Function-Gaussian (RBF-G) model with optical fitting data for wavefront reconstruction.
    • Created a digital twin platform for real-time monitoring and visualization.

    Main Results:

    • Achieved effective collaborative optimization with three objectives and three constraints using only 100 finite element analyses.
    • Obtained Pareto solutions with superior accuracy, comprehensiveness, and diversity compared to other algorithms.
    • Enabled fast wavefront reconstruction (2 s, R²=0.999) and real-time full-field deformation visualization (~50 ms).

    Conclusions:

    • The developed data-fused modeling, optimization, and visualization approach significantly advances optomechanical system design.
    • The CNSGAIII-EHVI algorithm offers an efficient method for complex multi-objective optimization.
    • The digital twin platform provides effective real-time performance monitoring and visualization capabilities.