Feed-forward neural network as nonlinear dynamics integrator for supercontinuum generation

Lauri Salmela, Mathilde Hary, Mehdi Mabed, Alessandro Foi, John M. Dudley, Goëry Genty

Tutkimustuotos: ArtikkeliTieteellinenvertaisarvioitu

26 Sitaatiot (Scopus)
29 Lataukset (Pure)

Abstrakti

The nonlinear propagation of ultrashort pulses in optical fibers depends sensitively on the input pulse and fiber parameters. As a result, the optimization of propagation for specific applications generally requires time-consuming simulations based on the sequential integration of the generalized nonlinear Schrödinger equation (GNLSE). Here, we train a feed-forward neural network to learn the differential propagation dynamics of the GNLSE, allowing emulation of direct numerical integration of fiber propagation, and particularly the highly complex case of supercontinuum generation. Comparison with a recurrent neural network shows that the feed-forward approach yields faster training and computation, and reduced memory requirements. The approach is generic and can be extended to other physical systems.

AlkuperäiskieliEnglanti
Sivut802-805
Sivumäärä4
JulkaisuOptics Letters
Vuosikerta47
Numero4
DOI - pysyväislinkit
TilaJulkaistu - 15 helmik. 2022
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Julkaisufoorumi-taso

  • Jufo-taso 2

!!ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

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