Predicting the largest Lyapunov exponent of chaotic optically injected lasers by machine learning

Area of Science:

• Nonlinear Dynamics
• Laser Physics
• Artificial Intelligence

Background:

• Chaotic semiconductor lasers exhibit complex dynamics.
• Accurate prediction of chaotic behavior is crucial for laser applications.
• Traditional methods for estimating chaotic parameters are computationally intensive.

Purpose of the Study:

• To develop a novel, efficient method for predicting the largest Lyapunov exponent (LLE) in chaotic semiconductor lasers.
• To leverage convolutional neural networks (CNNs) for analyzing chaotic laser emission intensity waveforms.
• To assess the accuracy and computational advantages of the proposed AI-driven approach compared to traditional methods.

Main Methods:

• Generated chaotic emission intensity waveforms from an optically injected laser.
• Calculated LLEs using a traditional phase-space reconstruction method for training data.
• Trained a convolutional neural network (CNN) model with optimized parameters to predict LLEs.
• Validated the CNN model's performance using both simulations and experimental data.

Main Results:

• Achieved successful prediction of LLE with less than 5% error in both simulations and experiments.
• Reduced computation time from approximately 100 seconds to under 1 second, an improvement of over 99%.
• Decreased the required input data length by 80% compared to traditional methods.
• Investigated the impact of inherent laser and measurement noise on prediction accuracy.

Conclusions:

• The developed CNN-based method provides a highly accurate and efficient approach for LLE prediction in chaotic semiconductor lasers.
• This AI-driven technique significantly enhances computational efficiency, reducing analysis time and data requirements.
• The study offers a new perspective and a powerful tool for understanding and analyzing complex laser dynamics.