| Issue |
EPJ Web Conf.
Volume 287, 2023
EOS Annual Meeting (EOSAM 2023)
|
|
|---|---|---|
| Article Number | 13001 | |
| Number of page(s) | 2 | |
| Section | Focused Sessions (FS) 4- Machine Learning and Photonic Artificial Intelligence / Optical Neural Networks and Neuromorphic Computing | |
| DOI | https://doi.org/10.1051/epjconf/202328713001 | |
| Published online | 18 October 2023 | |
https://doi.org/10.1051/epjconf/202328713001
Analysing interaction and localization dynamics in modulation instability via data-driven dominant balance
1 Université de Franche-Comté, Institut FEMTO-ST, CNRS UMR 6174, Besançon, France
2 Université de Bourgogne, Laboratoire Interdisciplinaire Carnot de Bourgogne, CNRS UMR 6303, 21078 Dijon, France
3 Photonics Laboratory, Tampere University, Tampere, FI-33104, Finland
* Corresponding author: andrei.ermolaev@femto-st.fr
Published online: 18 October 2023
We report the first application of the Machine Learning technique of data-driven dominant balance to optical fiber noise-driven Modulation Instability, with the aim to automatically identify local regions of dispersive and nonlinear interactions governing the dynamics. We first consider the analytical solutions of Nonlinear Schrödinger Equation – solitons on finite background – where it is shown that dominant balance distinguishes two particularly different dynamical regimes: one where the nonlinear process is dominating the dispersive propagation, and one where nonlinearity and second order dispersion act together driving the localization of breathers. By means of numerical simulations, we then analyse the spatio-temporal dynamics of noise-driven Modulation Instability and demonstrate that data-driven dominant balance can successfully identify the associated dominating physical regimes even within the turbulent dynamics.
© The Authors, published by EDP Sciences, 2023
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