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We use a machine-learning based paradigm to solve the direct and inverse problems relating to the shaping of optical pulses that occurs upon nonlinear propagation in optical fibres with a neural network.

We demonstrate different types of breathing soliton complexes in a mode-locked fibre laser: multi-breather molecules, and molecular complexes arising from the binding of two breather-pair molecules or a breather-pair molecule and a single breather.

We present a general method for the design of fibre-based nonlinear pulse shaping. By combining direct numerical simulations with machine-learning strategies, we efficiently identify the optimal working parameters for achieving a given pulse target.

We apply predictive regression to find optimum operating regimes in a recently proposed layout of a flexible Figure-8 laser having two independently pumped segments of active fibre in its bidirectional ring.

We report on our experimental and theoretical results on the use of a sinusoidally time varying phase to suppress undesirable self-phase modulation of optical pulses propagating in fiber-optic systems.

We numerically show the possibility of pulse shaping in a mode-locked fiber laser by inclusion of an amplitude-phase spectral filter into the laser cavity. Various advanced temporal waveforms are generated, including parabolic, flat-top and triangular pulses.

We propose and numerically demonstrate a new concept of fibre laser architecture supporting self-similar pulse evolution in the amplifier and nonlinear pulse spectral compression in the passive fibre. The latter process is beneficial for improving the power efficiency as it prevents strong spectral filtering from being highly dissipative.