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Coupling rare event algorithms and deep neural network to predict extreme heat waves
Multi angle
Authors : Bouchet, Freddy (Author of the conference)
CIRM (Publisher )
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
Additional resources :
https://www.cirm-math.fr/RepOrga/2389/Slides/Bouchet.pdf
CIRM (Publisher )
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Abstract :
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.
MSC Codes : 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
Additional resources :
https://www.cirm-math.fr/RepOrga/2389/Slides/Bouchet.pdf
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Information on the Video
Film maker : Hennenfent, GuillaumeLanguage : English Available date : 18/11/2021 Conference Date : 27/09/2021 Subseries : Research talks arXiv category : Statistical Mechanics ; Mathematical Physics Mathematical Area(s) : Mathematical Physics ; Probability & Statistics Format : MP4 (.mp4) - HD Video Time : 00:50:12 Targeted Audience : Researchers Download : https://videos.cirm-math.fr/2021-09-27_Bouchet.mp4 
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Information on the Event
Event Title : On Future Synergies for Stochastic and Learning Algorithms / Sur les synergies futures autour des algorithmes d'apprentissage et stochastiquesEvent Organizers : Durmus, Alain ; Michel, Manon ; Roberts, Gareth ; Zdeborova, Lenka Dates : 27/09/2021 - 01/10/2021 Event Year : 2021 Event URL : https://conferences.cirm-math.fr/2389.html
Citation Data
DOI : 10.24350/CIRM.V.19817403Cite this video as: 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 |
See Also
- [Multi angle] MCMC for systems with glassy dynamics / Author of the conference Berthier, Ludovic.
- [Post-edited] Biased Monte Carlo sampling in RBMs / Author of the conference Seoane, Beatriz.
- [Multi angle] Irreversible Monte Carlo methods for particle simulations / Author of the conference Maggs, Anthony.
- [Multi angle] Molecular dynamics algorithms: numerical and mathematical analysis / Author of the conference Lelièvre, Tony.
Bibliography
- 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
