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Communication Dans Un Congrès Année : 2023

Machine Learning-assisted spatiotemporal chaos forecasting

Résumé

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.

Dates et versions

hal-04505198 , version 1 (14-03-2024)

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Citer

Georges Murr, Saliya Coulibaly. Machine Learning-assisted spatiotemporal chaos forecasting. EOS Annual Meeting (EOSAM 2023), Sep 2023, Dijon, France. pp.13002, ⟨10.1051/epjconf/202328713002⟩. ⟨hal-04505198⟩
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