Machine learning algorithms for predicting the amplitude of chaotic laser pulses

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.

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• Model selection of chaotic systems from data with hidden variables using sparse data assimilation
• Model-free prediction of emergence of extreme events in a parametrically driven nonlinear dynamical system by Deep Learning
• Extreme events prediction from nonlocal partial information in a spatiotemporally chaotic microcavity laser
• Extreme rotational events in a forced-damped nonlinear pendulum
• Precursor-driven machine learning prediction of chaotic extreme pulses in Kerr resonators
• Measuring the rogue wave pattern triggered from Gaussian perturbations by deep learning