Machine learning algorithms for predicting the amplitude of chaotic laser pulses

Area of Science:

• Physics
• Nonlinear Dynamics
• Machine Learning

Background:

• Forecasting chaotic system dynamics from output signals is crucial across scientific fields.
• Optically injected semiconductor lasers exhibit complex dynamics, including ultrahigh intensity pulses resembling rogue waves.

Purpose of the Study:

• To compare the forecasting performance of various machine learning algorithms for chaotic laser pulse amplitudes.
• To evaluate the impact of noise and training data length on prediction accuracy.

Main Methods:

• Simulated dynamics of an optically injected semiconductor laser.
• Applied and compared deep learning, support vector machine, nearest neighbors, and reservoir computing algorithms.
• Analyzed prediction accuracy based on varying noise levels and training dataset sizes.

Main Results:

• Reservoir computing demonstrated superior performance in forecasting chaotic laser pulse amplitudes compared to other methods.
• Prediction accuracy was sensitive to noise levels and the length of the time series used for training.
• All tested algorithms showed a decrease in performance with increased noise and reduced training data.

Conclusions:

• Machine learning, particularly reservoir computing, offers a viable approach for predicting chaotic laser dynamics.
• The robustness of forecasting models is significantly influenced by signal-to-noise ratio and data availability.
• Further research can optimize these methods for real-world applications in chaotic system analysis.