Intermittent dynamics identification and prediction from experimental data of discrete-mode semiconductor lasers by...

Area of Science:

• Nonlinear dynamics
• Complex systems analysis
• Laser physics

Background:

• Understanding intermittent dynamics in complex nonlinear systems is crucial.
• Experimental data analysis is key to uncovering underlying physical mechanisms.
• Discrete-mode semiconductor lasers exhibit complex intermittent switching behaviors.

Purpose of the Study:

• To demonstrate reservoir computing for predicting and identifying intermittent switching dynamics.
• To analyze experimental data from discrete-mode semiconductor lasers.
• To compare reservoir computing performance against conventional methods.

Main Methods:

• Reservoir computing (RC) for time-series prediction and classification.
• Analysis of experimental data from discrete-mode semiconductor lasers.
• Evaluation of RC performance using normalized mean-square error and binary accuracy.

Main Results:

• RC reliably predicts regular and irregular intermittent switching with <0.015 mean-square error.
• RC achieves >0.996 accuracy in identifying both switching types.
• RC outperforms amplitude threshold methods, particularly in transient regions.

Conclusions:

• Reservoir computing is a powerful tool for analyzing complex nonlinear dynamics.
• RC offers superior accuracy for predicting and identifying intermittent switching in laser systems.
• The study highlights RC's effectiveness in practical experimental data analysis.