Data-driven model discovery of ideal four-wave mixing in nonlinear fibre optics.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
26 Jul 2022
26 Jul 2022
Historique:
received:
01
06
2022
accepted:
12
07
2022
entrez:
26
7
2022
pubmed:
27
7
2022
medline:
27
7
2022
Statut:
epublish
Résumé
We show using numerical simulations that data driven discovery using sparse regression can be used to extract the governing differential equation model of ideal four-wave mixing in a nonlinear Schrödinger equation optical fibre system. Specifically, we consider the evolution of a strong single frequency pump interacting with two frequency detuned sidebands where the dynamics are governed by a reduced Hamiltonian system describing pump-sideband coupling. Based only on generated dynamical data from this system, sparse regression successfully recovers the underlying physical model, fully capturing the dynamical landscape on both sides of the system separatrix. We also discuss how analysing an ensemble over different initial conditions allows us to reliably identify the governing model in the presence of noise. These results extend the use of data driven discovery to ideal four-wave mixing in nonlinear Schrödinger equation systems.
Identifiants
pubmed: 35882898
doi: 10.1038/s41598-022-16586-5
pii: 10.1038/s41598-022-16586-5
pmc: PMC9325870
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
12711Subventions
Organisme : Centre National de la Recherche Scientifique
ID : MITI Evenements Rares 2022
Organisme : Centre National de la Recherche Scientifique
ID : MITI Evenements Rares 2022
Organisme : Centre National de la Recherche Scientifique
ID : MITI Evenements Rares 2022
Organisme : Centre National de la Recherche Scientifique
ID : MITI Evenements Rares 2022
Organisme : Agence Nationale de la Recherche
ID : ANR15-IDEX-0003
Organisme : Agence Nationale de la Recherche
ID : ANR15-IDEX-0003
Organisme : Agence Nationale de la Recherche
ID : ANR15-IDEX-0003
Organisme : Agence Nationale de la Recherche
ID : ANR15-IDEX-0003
Organisme : Academy of Finland
ID : 318082
Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2022. The Author(s).
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