Machine learning for prediction of extreme statistics in modulation instability
This work addresses the challenge of incomplete characterization in nonlinear fiber optics experiments, offering a method to infer temporal properties from spectral data, which is incremental but novel for this domain.
The paper tackled the problem of predicting extreme temporal statistics in modulation instability using only spectral data, achieving predictions of the probability distribution for maximum intensity peaks from experimental spectral measurements.
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.