Performance analysis of dual-pump nonlinear amplifying loop mirror mode-locked all-fibre laser

Boscolo, Sonia; Finot, Christophe; Gukov, Ilya; Turitsyn, Sergei K.

doi:10.1088/1612-202x/ab1bd7

Cited by 12 publications

(8 citation statements)

References 25 publications

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“…Owing to its power of extracting essential information from large amounts of data, machine learning is bringing a revolutionary reform to research in the physical sciences [8]. In the field of photonics, a number of studies have been recently reported in laser design and optimization [9][10][11], complex nonlinear dynamics [12], design of photonic crystal fibers and optical components [13,14], pulse characterization [15], and optical communications [16,17]. In [18], we have shown that the combination of a graphical approach with the machine-learning method of neural networks (NNs) can provide a rapid and precise identification of the parameters of nonlinear pulse shaping systems based on pulse propagation in a normally dispersive fiber that are required to generate pulses with preset temporal features.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks for nonlinear pulse shaping in optical fibers

Boscolo

Finot

2020

Optics & Laser Technology

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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.

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Section: Introductionmentioning

confidence: 99%

Artificial neural networks for nonlinear pulse shaping in optical fibers

Boscolo

Finot

2020

Optics & Laser Technology

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“…Moreover, it enables reliable and reproducible live electronic adjustment of the lasing regimes. In this work, we numerically explore the broad range of operating states of the laser that can be accessed through independent control of the pump powers in the two gain segments and the laser output coupling ratio, β [12]. We use a piece-wise propagation model for the laser, in which propagation in the fibres follows a standard modified NLSE including gain saturation and spectral response for the active segments.…”

Section: Nonlinear Sculpuring In An All-fiber Figure-8 Lasermentioning

confidence: 99%

Optical Waveform Tailoring in Passive and Laser Cavity Fibre Systems

Boscolo

Gukov

Finot

2019

2019 21st International Conference on Transparent Optical Networks (ICTON)

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The interplay among the effects of dispersion, nonlinearity and gain/loss in optical fibres is a powerful tool to generate a broad range of pulse shapes with tuneable properties. Here we propose a method to optimise the systems parameters for a given pulse target. By reducing the system complexity and applying machine-learning strategies, we show that it is possible to efficiently identify the sets of parameters of interest. Two configurations are numerically investigated: pulse shaping in a passive normally dispersive fibre and pulse generation in a dualpump nonlinear-amplifying-loop-mirror mode-locked fibre laser.

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“…Machine learning algorithms have been already shown to be highly effective in solving optimization problems arising in photonics 23 – 27 . Particular type of the optimization algorithm should be chosen taking into account specific features of the fitness function.…”

Section: Introductionmentioning

confidence: 99%

Inverse design of mode-locked fiber laser by particle swarm optimization algorithm

Kokhanovskiy

Kuprikov

Bednyakova

et al. 2021

Sci Rep

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A wide variety of laser applications, that often require radiation with specific characteristics, and relative flexibility of laser configurations offer a prospect of designing systems with the parameters on demand. The inverse laser design problem is to find the system architecture that provides for the generation of the desired laser output. However, typically, such inverse problems for nonlinear systems are sensitive to the computation of the gradients of a target (fitness) function making direct back propagation approach challenging. We apply here particle swarm optimization algorithm that does not rely on the gradients of the fitness function to the design of a fiber 8-figure laser cavity. This technique allows us to determine the laser cavity architectures tailored to generating on demand pulses with duration in the range of 1.5–105 ps and spectral width in the interval 0.1–20.5 nm. The proposed design optimisation algorithm can be applied to a variety of laser applications, and, more generally, in a range of engineering systems with flexible adjustable configurations and the outputs on demand.

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Performance analysis of dual-pump nonlinear amplifying loop mirror mode-locked all-fibre laser

Cited by 12 publications

References 25 publications

Artificial neural networks for nonlinear pulse shaping in optical fibers

Artificial neural networks for nonlinear pulse shaping in optical fibers

Optical Waveform Tailoring in Passive and Laser Cavity Fibre Systems

Inverse design of mode-locked fiber laser by particle swarm optimization algorithm

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