Machine learning applications to nonlinear fiber-optics systems

We will review our work in the field of smart ultrafast photonics where machine-learning algorithms are combined with nonlinear optical systems allowing for optimized performance and control. In particular, we will show how the techniques of machine learning can be efficiently exploited for the analysis of nonlinear instabilities; the prediction of complex supercontinuum generation dynamics with orders of magnitude increased computation speed when compared to conventional direct numerical integration; the optimized and precise control of the spectrum of broadband supercontinuum sources.