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Article Dans Une Revue Scientific Reports Année : 2023

Analysis of interaction dynamics and rogue wave localization in modulation instability using data-driven dominant balance

Résumé

Abstract We analyze the dynamics of modulation instability in optical fiber (or any other nonlinear Schrödinger equation system) using the machine-learning technique of data-driven dominant balance. We aim to automate the identification of which particular physical processes drive propagation in different regimes, a task usually performed using intuition and comparison with asymptotic limits. We first apply the method to interpret known analytic results describing Akhmediev breather, Kuznetsov-Ma, and Peregrine soliton (rogue wave) structures, and show how we can automatically distinguish regions of dominant nonlinear propagation from regions where nonlinearity and dispersion combine to drive the observed spatio-temporal localization. Using numerical simulations, we then apply the technique to the more complex case of noise-driven spontaneous modulation instability, and show that we can readily isolate different regimes of dominant physical interactions, even within the dynamics of chaotic propagation.
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Origine : Publication financée par une institution
Licence : CC BY - Paternité

Dates et versions

hal-04145188 , version 1 (21-12-2023)

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Andrei V Ermolaev, Mehdi Mabed, Christophe Finot, Goëry Genty, J.M. Dudley. Analysis of interaction dynamics and rogue wave localization in modulation instability using data-driven dominant balance. Scientific Reports, 2023, 13 (1), pp.10462. ⟨10.1038/s41598-023-37039-7⟩. ⟨hal-04145188⟩
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