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Communication Dans Un Congrès Année : 2023

Machine learning-assisted extreme events forecasting in Kerr ring resonators

Résumé

Predicting complex nonlinear dynamical systems has been even more urgent because of the emergence of extreme events such as earthquakes, volcanic eruptions, extreme weather events (lightning, hurricanes/cyclones, blizzards, tornadoes), and giant oceanic rogue waves, to mention a few. The recent milestones in the machine learning framework o↵er a new prospect in this area. For a high dimensional chaotic system, increasing the system’s size causes an augmentation of the complexity and, finally, the size of the artificial neural network. Here, we propose a new supervised machine learning strategy to locally forecast bursts occurring in the turbulent regime of a fiber ring cavity.

Dates et versions

hal-04505189 , version 1 (14-03-2024)

Identifiants

Citer

Saliya Coulibaly, Florent Bessin, Marcel Clerc, Arnaud Mussot. Machine learning-assisted extreme events forecasting in Kerr ring resonators. EOS Annual Meeting (EOSAM 2023), Sep 2023, Dijon, France. pp.08015, ⟨10.1051/epjconf/202328708015⟩. ⟨hal-04505189⟩
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