Predicting extreme events and statistics in modulation instability using machine learning (Conference Presentation)

Goëry Genty; Lauri Salemla; Mikko Närhi; Cyril Billet; Juha Toivonen; John M. Dudley

doi:10.1117/12.2507963

4 March 2019 Predicting extreme events and statistics in modulation instability using machine learning (Conference Presentation)

Goëry Genty, Lauri Salemla, Mikko Närhi, Cyril Billet, Juha Toivonen, John M. Dudley

Author Affiliations +

Proceedings Volume 10903, Real-time Measurements, Rogue Phenomena, and Single-Shot Applications IV; 1090303 (2019) https://doi.org/10.1117/12.2507963
Event: SPIE LASE, 2019, San Francisco, California, United States

Abstract

Modulation instability (MI) is one of the most nonlinear instability of the focusing nonlinear Schrödinger equation that describes how a low amplitude noise on an input signal can be exponentially amplified to create high-intensity localised structures. Recently, MI has attracted renewed attention due to its potential link with the development of extreme events or rogue waves, and many theoretical, numerical and experimental studies have been reported in various physical systems including optics and hydrodynamics. A particular difficulty associated with experimental studies of MI in optics is the need for real-time measurement techniques. In the time domain, the time-lens approach is complex and constrains the measurement bandwidth and power. In the spectral domain, the dispersive Fourier transform is simpler but only typically allows for low dynamic range measurements and does not provide information about the associated temporal properties. We report on our recent work on the use of machine learning to predict from real-time spectral data statistics for the maximum intensity of the localised temporal peaks in a fibre-optic chaotic MI field, peaks which are preferentially associated with extreme events. We subsequently train a neural network to correlate the spectral and temporal properties using data from numerical simulations and we use this model to predict the temporal probability distribution based on near 60 dB dynamic range real-time spectral data from experiments. These results open novel perspectives in all systems exhibiting chaos and instability where direct time-domain observations are difficult.

Conference Presentation

Citation Download Citation

Goëry Genty, Lauri Salemla, Mikko Närhi, Cyril Billet, Juha Toivonen, and John M. Dudley "Predicting extreme events and statistics in modulation instability using machine learning (Conference Presentation)", Proc. SPIE 10903, Real-time Measurements, Rogue Phenomena, and Single-Shot Applications IV, 1090303 (4 March 2019); https://doi.org/10.1117/12.2507963

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KEYWORDS

Machine learning

Modulation

Data modeling

Nonlinear optics

Chaos

Fourier transforms

Interference (communication)

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