We review the use of machine learning techniques in ultrafast dynamics in fiber-optics systems. We discuss how neural networks can be used to correlate the spectral and temporal characteristics of dissipative soliton lasers and predict nonlinear dynamics in optical fibers for a wide range of input conditions. We also show how machine learning algorithm allow for optimizing supercontinuum generation.
We use machine learning methods to control the spectral broadening experienced by femtosecond pulses in a highly nonlinear fiber. Combining a programmable spectral filter with a genetic algorithm or neural network allows us to optimize the nonlinear propagation dynamics to generate an on-demand target spectrum. Our approach is generic and can be adapted to a wide range of optical fibers and pump pulses. We expect our results to provide significant advances for adaptative control and tailored light sources.
Although the successes of artificial intelligence in areas such as automatic translation are well known, the application of the powerful techniques of deep learning to current optics research is at a comparatively early stage. However, an area with particular promise for deep learning to accelerate both basic science and applications is in ultrafast optics, where nonlinear light-matter interactions lead to highly complex dynamics, including the emergence of extreme events. In the particular field of nonlinear fibre optics, we have recently reported a number of results that have shown how deep learning can both augment existing experimental techniques as well as provide new theoretical insights into the underlying physics. The objective of this paper is to review a selection of our work in this area.
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