English
Coupling rare event algorithms and deep neural network to predict extreme heat waves
Multi angle
Auteurs : Bouchet, Freddy (Auteur de la Conférence)
CIRM (Editeur )
00A79 - Physics (use more specific entries from Sections 70 through 86 when possible)
60F10 - Large deviations
68T01 - General
70K99 - None of the above but in this section
86A10 - Meteorology and atmospheric physics
Ressources complémentaires :
https://www.cirm-math.fr/RepOrga/2389/Slides/Bouchet.pdf
CIRM (Editeur )
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Résumé :
Extreme events are of primarily importance for understanding the impact of climate change. However, because they are too rare and realistic models are too complex, traditional deep neural networks are inefficient for predictions. We cope with this lack of data using rare event simulations. From the best climate models, we oversample extremely rare events and obtain several hundreds more events than with usual climate runs, at a fixed numerical cost. Coupled with deep neural networks this approach improves drastically the prediction of extreme heat waves.
Codes MSC : 00A79 - Physics (use more specific entries from Sections 70 through 86 when possible)
60F10 - Large deviations
68T01 - General
70K99 - None of the above but in this section
86A10 - Meteorology and atmospheric physics
Ressources complémentaires :
https://www.cirm-math.fr/RepOrga/2389/Slides/Bouchet.pdf
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Informations sur la Vidéo
Réalisateur : Hennenfent, GuillaumeLangue : Anglais Date de publication : 18/11/2021 Date de captation : 27/09/2021 Sous collection : Research talks arXiv category : Statistical Mechanics ; Mathematical Physics Domaine : Mathematical Physics ; Probability & Statistics Format : MP4 (.mp4) - HD Durée : 00:50:12 Audience : Researchers Download : https://videos.cirm-math.fr/2021-09-27_Bouchet.mp4 
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Informations sur la Rencontre
Nom de la rencontre : On Future Synergies for Stochastic and Learning Algorithms / Sur les synergies futures autour des algorithmes d'apprentissage et stochastiquesOrganisateurs de la rencontre : Durmus, Alain ; Michel, Manon ; Roberts, Gareth ; Zdeborova, Lenka Dates : 27/09/2021 - 01/10/2021 Année de la rencontre : 2021 URL Congrès : https://conferences.cirm-math.fr/2389.html
Données de citation
DOI : 10.24350/CIRM.V.19817403Citer cette vidéo: Bouchet, Freddy (2021). Coupling rare event algorithms and deep neural network to predict extreme heat waves. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19817403 URI : http://dx.doi.org/10.24350/CIRM.V.19817403 |
Voir aussi
- [Multi angle] MCMC for systems with glassy dynamics / Auteur de la Conférence Berthier, Ludovic.
- [ Post-edited] Biased Monte Carlo sampling in RBMs / Auteur de la Conférence Seoane, Beatriz.
- [Multi angle] Irreversible Monte Carlo methods for particle simulations / Auteur de la Conférence Maggs, Anthony.
- [Multi angle] Molecular dynamics algorithms: numerical and mathematical analysis / Auteur de la Conférence Lelièvre, Tony.
Bibliographie
- RAGONE, Francesco, WOUTERS, Jeroen, et BOUCHET, Freddy. Computation of extreme heat waves in climate models using a large deviation algorithm. Proceedings of the National Academy of Sciences, 2018, vol. 115, no 1, p. 24-29. - https://doi.org/10.1073/pnas.1712645115
- LUCENTE, Dario, ROLLAND, Joran, HERBERT, Corentin, et al. Coupling rare event algorithms with data-based learned committor functions using the analogue Markov chain. arXiv preprint arXiv:2110.05050, 2021. - https://arxiv.org/abs/2110.05050
- JACQUES-DUMAS, Valérian, RAGONE, Francesco, BORGNAT, Pierre, et al. Deep Learning-based Extreme Heatwave Forecast. arXiv preprint arXiv:2103.09743, 2021. - https://arxiv.org/abs/2103.09743
