Machine learning-assisted extreme events forecasting in Kerr ring resonators

Coulibaly, Saliya; Bessin, Florent; Clerc, Marcel; Mussot, Arnaud

Document type :

Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes

DOI :

10.1051/epjconf/202328708015

Title :

Machine learning-assisted extreme events forecasting in Kerr ring resonators

Author(s) :

Coulibaly, Saliya [Auteur]

Laboratoire de Physique des Lasers, Atomes et Molécules - UMR 8523 [PhLAM]
Bessin, Florent [Auteur]
Laboratoire de Photonique d'Angers [LPHIA]
Clerc, Marcel [Auteur]
Departamento de Física [Santiago de Chile] [DFI-FCFM]
Mussot, Arnaud [Auteur]

Laboratoire de Physique des Lasers, Atomes et Molécules - UMR 8523 [PhLAM]

Conference title :

EOS Annual Meeting (EOSAM 2023)

City :

Dijon

Country :

France

Start date of the conference :

2023-09-11

Publication date :

2023-10-18

HAL domain(s) :

Science non linéaire [physics]/Dynamique Chaotique [nlin.CD]

English abstract : [en]

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, ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Laboratoire de Physique des Lasers, Atomes et Molécules (PhLAM) - UMR 8523

Source :

Harvested from HAL

Files

epjconf_eosam2023_08015.pdf
Open access
Access the document

Machine learning-assisted extreme events ... BibTeX CSV Excel RIS

Files

Machine learning-assisted extreme events ...

BibTeX

CSV

Excel

RIS