Geodesic
Partial differential equations for oceanic artificial intelligence
Jules Guillot1 ; Guillaume Koenig2 ; Kadi Minbashian3 ; Emmanuel Frénod4 ; Héléne Flourent1 ; Julien Brajard5
1LMBA/UMR 6205, University of South Brittany
2MIO, University of Marseille
3Numerical Analysis and Scientific Computing, Department of Mathematics, Technical University of Darmstadt
4LMBA/UMR 6205, University of South Brittany and See-d, Vannes
5LOCEAN, IPSL
ESAIM. Proceedings, Tome 70 (2021), pp. 137-146
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The Sea Surface Temperature (SST) plays a significant role in analyzing and assessing the dynamics of weather and also biological systems. It has various applications such as weather forecasting or planning of coastal activities. On the one hand, standard physical methods for forecasting SST use coupled ocean- atmosphere prediction systems, based on the Navier-Stokes equations. These models rely on multiple physical hypotheses and do not optimally exploit the information available in the data.On the other hand, despite the availability of large amounts of data, direct applications of machine learning methods do not always lead to competitive state of the art results. Another approach is to combine these two methods: this is data-model coupling. The aim of this paper is to use a model in another domain. This model is based on a data-model coupling approach to simulate and predict SST. We first introduce the original model. Then, the modified model is described, to finish with some numerical results.
DOI : 10.1051/proc/202107009

Jules Guillot  1   ; Guillaume Koenig  2   ; Kadi Minbashian  3   ; Emmanuel Frénod  4   ; Héléne Flourent  1   ; Julien Brajard  5

1 LMBA/UMR 6205, University of South Brittany
2 MIO, University of Marseille
3 Numerical Analysis and Scientific Computing, Department of Mathematics, Technical University of Darmstadt
4 LMBA/UMR 6205, University of South Brittany and See-d, Vannes
5 LOCEAN, IPSL 
@article{EP_2021_70_a9,
     author = {Jules Guillot and Guillaume Koenig and Kadi Minbashian and Emmanuel Fr\'enod and H\'el\'ene Flourent and Julien Brajard},
     title = {Partial differential equations for oceanic artificial intelligence},
     journal = {ESAIM. Proceedings},
     pages = {137--146},
     year = {2021},
     volume = {70},
     doi = {10.1051/proc/202107009},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.1051/proc/202107009/}
}
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%T Partial differential equations for oceanic artificial intelligence
%J ESAIM. Proceedings
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Jules Guillot; Guillaume Koenig; Kadi Minbashian; Emmanuel Frénod; Héléne Flourent; Julien Brajard. Partial differential equations for oceanic artificial intelligence. ESAIM. Proceedings, Tome 70 (2021), pp. 137-146. doi: 10.1051/proc/202107009

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