Abstract

The interplay among the effects of dispersion, nonlinearity and gain/loss in optical fibre systems can be efficiently used to shape the pulses and manipulate and control the light dynamics and, hence, lead to different pulse-shaping regimes [1,2]. However, achieving a precise waveform with various prescribed characteristics is a complex issue that requires careful choice of the initial pulse conditions and system parameters. The general problem of optimisation towards a target operational regime in a complex multi-parameter space can be intelligently addressed by implementing machine-learning strategies. In this paper, we discuss a novel approach to the characterisation and optimisation of nonlinear shaping in fibre systems, which combines numerical simulations of the governing equations to identify the relevant parameters and the machine-learning method of neural networks (NNs) to make predictions across a larger range of the data domain. We illustrate this general method through application to two configurations.

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