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Propagation non-linéaire dans des fibres optiques par réseaux de neurones artificiels

Authors: Boscolo, Sonia; Finot, Christophe;

Propagation non-linéaire dans des fibres optiques par réseaux de neurones artificiels

Abstract

National audience; Les techniques d’apprentissage machine transforment le paysage de la recherche traditionnelle avec l’utilisation d’outils algorithmiques avancés pour l’analyse de données massives offrant de nouveaux angles de vue. Les domaines de la photonique et de l’optique ultra-rapide n’échappent pas à cette révolution. Nous nous intéressons dans cette contribution à la mise en œuvre de telles techniques appliquées à la mise en forme non-linéaire d’impulsions se propageant dans une fibre optique en présence de non-linéarités optiques. En effet, la combinaison de la dispersion et de la non-linéarité au cours de la propagation modifie profondément les profils temporels et spectraux de toute impulsion se propageant dans une fibre optique. Le résultat dépend tout autant des propriétés de la fibre utilisée que des propriétés de l’impulsion initiale et des impulsions ultra-brèves, triangulaires, paraboliques, super-gaussiennes peuvent être générées, tout comme des spectres significativement élargis ou, au contraire, comprimés. Dans tous ces processus, l’évolution du champ complexe lumineux dans une fibre monomode est prédite grâce à l’intégration numérique de l’équation de Schrödinger non-linéaire (ESNL). Nous montrons dans cette contribution qu’un réseau neuronal peut se substituer à cette approche et fournir fidèlement les profils temporels et spectraux d’intensité. Un réseau neuronal est également en mesure de résoudre le problème inverse, i.e. reconnaître à partir des profils d’intensité de l’impulsion, les conditions de la propagation.

Country
France
Keywords

[PHYS.PHYS.PHYS-OPTICS] Physics [physics]/Physics [physics]/Optics [physics.optics], [PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics]

[1] G. Genty, L. Salmela, J. M. Dudley, D. Brunner, A. Kokhanovskiy, S. Kobtsev, and S. K. Turitsyn, "Machine learning and applications in ultrafast photonics," Nat. Photon. 15, 91-101 (2021).

[2] S. Boscolo and C. Finot, Shaping Light in Nonlinear Optical Fibers (2017).

[3] C. Finot and S. Boscolo, "Design rules for nonlinear spectral compression in optical fibers," J. Opt. Soc. Am. B 33, 760-767 (2016). [OpenAIRE]

[4] C. Finot, I. Gukov, K. Hammani, and S. Boscolo, "Nonlinear sculpturing of optical pulses with normally dispersive fiber-based devices," Opt. Fiber Technol. 45, 306-312 (2018). [OpenAIRE]

[5] S. Boscolo and C. Finot, "Artificial neural networks for nonlinear pulse shaping in optical fibers," Opt. Laser Technol. 131, 106439 (2020).

[6] S. Boscolo, J. M. Dudley, and C. Finot, "Modelling self-similar parabolic pulses in optical fibres with a neural network," Results in Optics, 100066 (2021).

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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