Machine learning for prediction of extreme statistics in modulation instability

Abstract

A central area of research in nonlinear science is the study of instabilities that drive the emergence of extreme events. Unfortunately, experimental techniques for measuring such phenomena often provide only partial characterization. For example, real-time studies of instabilities in nonlinear fibre optics frequently use only spectral data, precluding detailed predictions about the associated temporal properties. Here, we show how Machine Learning can overcome this limitation by predicting statistics for the maximum intensity of temporal peaks in modulation instability based only on spectral measurements. Specifically, we train a neural network based Machine Learning model to correlate spectral and temporal properties of optical fibre modulation instability using data from numerical simulations, and we then use this model to predict the temporal probability distribution based on high-dynamic range spectral data from experiments. These results open novel perspectives in all systems exhibiting chaos and instability where direct time-domain observations are difficult.

References in corpus (6)

• Phase recovery and holographic image reconstruction using deep learning in neural networks
• Instabilities, breathers and rogue waves in optics
• Real-time observation of dissipative optical soliton molecular motions
• Direct observation of Rogue Waves in optical turbulence using Time Microscopy
• Real-time full bandwidth measurement of spectral noise in supercontinuum generation
• Deep Learning and Model Predictive Control for Self-Tuning Mode-Locked Lasers