Machine learning algorithms predict experimental output of chaotic lasers.

Area of Science:

• Nonlinear optics
• Machine learning
• Laser physics

Background:

• Experimental time series from lasers like Ti:sapphire and Nd:vanadate exhibit complex dynamics.
• Predicting these dynamics is crucial for laser stability and applications.
• Traditional methods face limitations in forecasting chaotic or extreme events.

Purpose of the Study:

• To investigate the efficacy of artificial neural networks (ANNs) for forecasting laser experimental time series.
• To predict pulse amplitude and pulse-to-pulse time separation in Ti:sapphire and Nd:vanadate lasers.
• To compare ANN predictive capabilities against the Lyapunov prediction horizon.

Main Methods:

• Application of a deep artificial neural network with 20 hidden layers.
• Utilizing backpropagation regression for time series forecasting.
• Testing the model on experimental data from a Kerr lens mode locking (KLM) Ti:sapphire laser and a Nd:vanadate laser with modulation losses.

Main Results:

• The ANN successfully predicted time series up to 10 steps ahead for both laser systems.
• Accurate pulse amplitude prediction was achieved for the Ti:sapphire laser, including extreme events.
• Both pulse amplitude and pulse-to-pulse time separation were forecasted for the Nd:vanadate laser.
• Predictions extended beyond the established Lyapunov prediction horizon for both lasers.

Conclusions:

• Deep artificial neural networks demonstrate robust predictive power for complex laser dynamics.
• ANNs offer a promising approach for forecasting laser behavior, outperforming conventional methods.
• This technique has implications for laser control, stability, and advanced applications.