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 achieve the generation of pulses with various specified temporal properties. The nonlinear shaping process is reduced to a numerical optimization problem over a three-dimensional space, where the intersections of different surfaces provide the means to quickly identify the sets of parameters of interest. We also show that the implementation of a machine-learning strategy can efficiently address the multi-parameter optimization problem being studied.
We numerically characterise, in the three-dimensional space of adjustable cavity parameters, the performance of a recently reported layout of a flexible figure-8 laser having two independently pumped segments of active fibre in its bidirectional ring [1]. We show that this optimisation problem can be efficiently addressed by applying a regression model based on a neural-network algorithm.
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