https://doi.org/10.1051/epjconf/202328713002
Machine Learning-assisted spatiotemporal chaos forecasting
Université de Lille, CNRS,UMR 8523-PhLAM-Physiques des Lasers Atomes et Molécules, F-(9000 Lille, France
* Corresponding author: georges.murr@ univ-lille.fr
Published online: 18 October 2023
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.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
