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


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.

SUBMITTER: Ermolaev AV 

PROVIDER: S-EPMC10307870 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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

Ermolaev Andrei V AV   Mabed Mehdi M   Finot Christophe C   Genty Goëry G   Dudley John M JM  

Scientific reports 20230628 1


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 Pe  ...[more]

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