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

Area of Science:

• Nonlinear Dynamics
• Laser Physics
• Computational Science

Background:

• Understanding intermittent dynamics in complex nonlinear systems is crucial.
• Experimental data analysis is key to uncovering underlying physical mechanisms.

Purpose of the Study:

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

Main Methods:

• Reservoir computing for dynamics prediction and identification.
• Analysis of experimental data from semiconductor lasers.
• 2-class classification for dynamic identification.

Main Results:

• Reservoir computing reliably predicted regular and irregular intermittent switching (average normalized mean-square error < 0.015).
• Identification accuracy for both switching types exceeded 0.996.
• Reservoir computing outperformed conventional amplitude threshold methods, especially in transient regions.

Conclusions:

• Reservoir computing is a powerful tool for analyzing complex nonlinear dynamics.
• The method offers superior accuracy for predicting and identifying intermittent switching in experimental data.
• This approach enhances the understanding of physical mechanisms in such systems.