Artificial neural networks for nonlinear pulse shaping in optical fibers

Abstract

We use a supervised machine-learning model based on a neural network to predict the temporal and spectral intensity profiles of the pulses that form upon nonlinear propagation in optical fibers with both normal and anomalous second-order dispersion. We also show that the model is able to retrieve the parameters of the nonlinear propagation from the pulses observed at the output of the fiber. Various initial pulse shapes as well as initially chirped pulses are investigated.

Nonlinear Pulse Shaping in Optical Fibres with a Neural Network

“…We have successfully used a supervised machine-learning model based on a NN to solve both the direct and inverse problems relating to the nonlinear pulse shaping in optical fibres. Details of this work can be found in [6]. Our results show that a properly trained network can greatly help the design and analysis of fibre-based shaping systems by providing immediate and sufficiently accurate solutions.…”

A neural network to explore the Fresnel diffraction of a sharp opaque semi-infinite screen

“…Without going into the details of the algorithms and with only a few lines of code, the students were able to identify the potential usefulness for the recognition or prediction of a diffraction pattern. The students were able to finish the project by reading some recent research papers using artificial intelligence to provide new advanced ways to engineer the various degrees of freedom of light [5,6].…”

Intelligent Breathing Soliton Generation in Ultrafast Fiber Lasers

“…Another promising area of research will be the expansion of the EA approach to use a wider range of tools in the general field of machine learning. Neural networks, for example, have previously been applied to the control of pulse shaping [56] and the classification of different regimes of nonlinear propagation [57,58] in single pass fibre geometries, and the optimisation of white-light continuum generation in bulk media. [59] Their extension to active control of mode locking has already been studied theoretically.…”