Machine learning algorithms for predicting the amplitude of chaotic laser pulses

TL;DR

This paper compares various machine learning algorithms in predicting the amplitude of chaotic laser pulses, focusing on their accuracy under different noise levels and training data lengths.

Contribution

It introduces a systematic comparison of deep learning, support vector machines, nearest neighbors, and reservoir computing for chaotic laser pulse prediction.

Findings

Reservoir computing outperforms other methods in accuracy.

Prediction accuracy decreases with increased noise.

Longer training sequences improve forecast performance.

Abstract

Forecasting the dynamics of chaotic systems from the analysis of their output signals is a challenging problem with applications in most fields of modern science. In this work, we use a laser model to compare the performance of several machine learning algorithms for forecasting the amplitude of upcoming emitted chaotic pulses. We simulate the dynamics of an optically injected semiconductor laser that presents a rich variety of dynamical regimes when changing the parameters. We focus on a particular dynamical regime that can show ultra-high intensity pulses, reminiscent of rogue waves. We compare the goodness of the forecast for several popular methods in machine learning, namely deep learning, support vector machine, nearest neighbors and reservoir computing. Finally, we analyze how their performance for predicting the height of the next optical pulse depends on the amount of noise and the length of the time-series used for training.