Cross-predicting the dynamics of an optically injected single-mode semiconductor laser using reservoir computing.

Area of Science:

• Physics
• Engineering
• Computer Science

Background:

• Real-world dynamical systems often have incomplete data due to technical limitations.
• This data gap hinders system monitoring, control, and decision-making.
• Cross-predicting missing variables is crucial for understanding and managing these systems.

Purpose of the Study:

• To apply a machine learning algorithm for cross-predicting unknown variables in a chaotic dynamical laser system.
• To reconstruct the dynamics of unmeasured variables using limited available data.
• To assess the accuracy and robustness of the proposed method.

Main Methods:

• Utilized a machine learning algorithm based on reservoir computing.
• Applied the algorithm to a realistic model of an optically injected single-mode semiconductor laser.
• Trained the algorithm with partial data and tested its predictive capabilities on unmeasured variables (electric field phase and carrier dynamics) from laser intensity.

Main Results:

• Demonstrated accurate reconstruction of two unmeasured dynamical variables from a single measured variable (laser intensity).
• Analyzed the impact of laser system and reservoir parameters on prediction accuracy.
• Validated the robustness of the cross-prediction method against noisy time series data.

Conclusions:

• Reservoir computing effectively reconstructs missing dynamical variables in chaotic laser systems.
• The developed method shows potential for time series reconstruction, data recovery, and secure data encryption applications.
• This approach offers a powerful tool for analyzing and controlling complex dynamical systems with limited observability.