التفاصيل البيبلوغرافية
| العنوان: |
Machine learning-assisted extreme events forecasting in Kerr ring resonators |
| المؤلفون: |
Coulibaly Saliya, Bessin Florent, Clerc Marcel, Mussot Arnaud |
| المصدر: |
EPJ Web of Conferences, Vol 287, p 08015 (2023) |
| بيانات النشر: |
EDP Sciences, 2023. |
| سنة النشر: |
2023 |
| المجموعة: |
LCC:Physics |
| مصطلحات موضوعية: |
Physics, QC1-999 |
| الوصف: |
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. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2100-014X |
| Relation: |
https://www.epj-conferences.org/articles/epjconf/pdf/2023/13/epjconf_eosam2023_08015.pdf; https://doaj.org/toc/2100-014X |
| DOI: |
10.1051/epjconf/202328708015 |
| URL الوصول: |
https://doaj.org/article/4b0ba66d935b4b568ccf15c1aebe632b |
| رقم الانضمام: |
edsdoj.4b0ba66d935b4b568ccf15c1aebe632b |
| قاعدة البيانات: |
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