Data-driven model discovery of ideal four-wave mixing in nonlinear fibre optics
Abstract 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 sin...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-16586-5 |
| _version_ | 1811223076599234560 |
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| author | Andrei V. Ermolaev Anastasiia Sheveleva Goëry Genty Christophe Finot John M. Dudley |
| author_facet | Andrei V. Ermolaev Anastasiia Sheveleva Goëry Genty Christophe Finot John M. Dudley |
| author_sort | Andrei V. Ermolaev |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-12T08:25:49Z |
| format | Article |
| id | doaj.art-d47c3f025f904c499a0f4424c306e471 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T08:25:49Z |
| publishDate | 2022-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-d47c3f025f904c499a0f4424c306e4712022-12-22T03:40:23ZengNature PortfolioScientific Reports2045-23222022-07-0112111110.1038/s41598-022-16586-5Data-driven model discovery of ideal four-wave mixing in nonlinear fibre opticsAndrei V. Ermolaev0Anastasiia Sheveleva1Goëry Genty2Christophe Finot3John M. Dudley4Institut FEMTO-ST, Université Bourgogne Franche-Comté CNRS UMR 6174Laboratoire Interdisciplinaire Carnot de Bourgogne, Université Bourgogne Franche-Comté CNRS UMR 6303Photonics Laboratory, Tampere UniversityLaboratoire Interdisciplinaire Carnot de Bourgogne, Université Bourgogne Franche-Comté CNRS UMR 6303Institut FEMTO-ST, Université Bourgogne Franche-Comté CNRS UMR 6174Abstract 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.https://doi.org/10.1038/s41598-022-16586-5 |
| spellingShingle | Andrei V. Ermolaev Anastasiia Sheveleva Goëry Genty Christophe Finot John M. Dudley Data-driven model discovery of ideal four-wave mixing in nonlinear fibre optics Scientific Reports |
| title | Data-driven model discovery of ideal four-wave mixing in nonlinear fibre optics |
| title_full | Data-driven model discovery of ideal four-wave mixing in nonlinear fibre optics |
| title_fullStr | Data-driven model discovery of ideal four-wave mixing in nonlinear fibre optics |
| title_full_unstemmed | Data-driven model discovery of ideal four-wave mixing in nonlinear fibre optics |
| title_short | Data-driven model discovery of ideal four-wave mixing in nonlinear fibre optics |
| title_sort | data driven model discovery of ideal four wave mixing in nonlinear fibre optics |
| url | https://doi.org/10.1038/s41598-022-16586-5 |
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