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    We developed a new modular model for simulating chirped pulse amplification (CPA) and nonlinear optical (NLO) laser systems. This approach enables data-driven machine learning for designing advanced laser systems for various applications.

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    Area of Science:

    • Laser Physics and Photonics
    • Computational Science
    • Materials Science

    Background:

    • Chirped pulse amplification (CPA) and nonlinear optical (NLO) systems are crucial for advancements in semiconductor manufacturing, communications, biology, and defense.
    • Accurate and efficient modeling of CPA+NLO laser systems is complex due to coupled processes and diverse simulation frameworks.

    Purpose of the Study:

    • To introduce a novel modular, start-to-end model for CPA+NLO laser systems.
    • To enable data-driven machine learning for optimization and inverse design of laser systems.
    • To demonstrate a new technical capability for creating tailored CPA+NLO systems.

    Main Methods:

    • Development of a modular, start-to-end simulation framework.
    • Integration of data-driven machine learning approaches for system design.
    • Application of the model to a representative high-power system, the LCLS-II photo-injector laser.

    Main Results:

    • The modular model successfully simulates complex CPA+NLO systems.
    • The approach facilitates new optimization and inverse design strategies.
    • Demonstrated capability for designing tailored laser systems beyond current model limitations.

    Conclusions:

    • The developed model offers a powerful new tool for advancing CPA+NLO laser system design.
    • This work opens avenues for machine learning-driven innovation in laser technology.
    • The LCLS-II photo-injector laser serves as a validated case study for the model's effectiveness.