Machine Learning-assisted spatiotemporal chaos forecasting

Murr, Georges; Coulibaly, Saliya

Document type :

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

DOI :

10.1051/epjconf/202328713002

Title :

Machine Learning-assisted spatiotemporal chaos forecasting

Author(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]

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]

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 ...
Show more >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.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

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