We expand our previous analysis of nonlinear pulse shaping in optical fibres using machine
learning [Opt. Laser Technol., 131 (2020) 106439] to the case of pulse propagation in the …

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 …

We numerically and experimentally demonstrate that pulses with a parabolic intensity profile
can be formed by passive reshaping of more conventional laser pulses using nonlinear …

The nonlinear evolution of ultrashort pulses in optical fiber has broad applications, but the
computational burden of convolutional numerical solutions necessitates rapid modeling …

We experimentally demonstrate the possibility to generate parabolic pulses via a single
dispersion decreasing optical fiber with normal dispersion. We numerically and …

The propagation of ultrashort pulses in optical fibre plays a central role in the development
of light sources and photonic technologies, with applications from fundamental studies of …

We present a general method to determine the parameters of nonlinear pulse shaping
systems based on pulse propagation in a normally dispersive fiber that are required to …

We deploy a supervised machine-learning model based on a neural network to predict the
temporal and spectral reshaping of a simple sinusoidal modulation into a pulse train having …

A physics-based deep learning (DL) method termed Phynet is proposed for modeling the
nonlinear pulse propagation in optical fibers totally independent of the ground truth. The …

An approximate analytical analysis is given describing the strongly nonlinear evolution of
optical pulses in nonlinear fibres with normal dispersion. The result provides a good explicit …