Machine Learning-assisted spatiotemporal chaos forecasting

Murr, Georges; Coulibaly, Saliya

Type de document :

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

DOI :

10.1051/epjconf/202328713002

Titre :

Machine Learning-assisted spatiotemporal chaos forecasting

Auteur(s) :

Murr, Georges [Auteur]
Laboratoire de Physique des Lasers, Atomes et Molécules - UMR 8523 [PhLAM]
Coulibaly, Saliya [Auteur]

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

Titre de la manifestation scientifique :

EOS Annual Meeting (EOSAM 2023)

Ville :

Dijon

Pays :

France

Date de début de la manifestation scientifique :

2023-09-11

Date de publication :

2023-10-18

Discipline(s) HAL :

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

Résumé en anglais : [en]

Long-term forecasting of extreme events such as oceanic rogue waves, heat waves, floods, earthquakes, has always been a challenge due to their highly complex dynamics. Recently, machine learning methods have been used for ...
Lire la suite >Long-term forecasting of extreme events such as oceanic rogue waves, heat waves, floods, earthquakes, has always been a challenge due to their highly complex dynamics. Recently, machine learning methods have been used for model-free forecasting of physical systems. In this work, we investigated the ability of these methods to forecast the emergence of extreme events in a spatiotemporal chaotic passive ring cavity by detecting the precursors of high intensity pulses. To this end, we have implemented supervised sequence (precursors) to sequence (pulses) machine learning algorithms, corresponding to a local forecasting of when and where extreme events will appear.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

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

Source :

Harvested from HAL

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epjconf_eosam2023_13002.pdf
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