Design, tuning, and blackbox optimization of laser systems

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.