Compensation of Carrier Envelope Phase Slip using Machine Learning

We demonstrate an effective compensation of carrier-envelope-phase (CEP) slip in a high-power femtosecond laser using a machine-learning (ML)-based control scheme.The compensation is achieved through fine dispersion tuning guided by a recurrent neural network (RNN) that predicts the temporal evolution of CEP slip, combined with a reinforcement-learning (RL) that determines the optimal corrective actions.With this RNN+RL framework, the integrated CEP noise is reduced by more than a factor of two compared with a conventional proportional-integral-derivative controller.The proposed ML-based control methodology provides a versatile tool for stabilizing and optimizing various parameters in high-power laser systems.