Partial differential equations for oceanic artificial intelligence
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 cou...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2021-01-01
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| Series: | ESAIM: Proceedings and Surveys |
| Subjects: | |
| Online Access: | https://www.esaim-proc.org/articles/proc/pdf/2021/01/proc2107009.pdf |
| _version_ | 1797971412075413504 |
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| author | Guillot Jules Koenig Guillaume Minbashian Kadi Frénod Emmanuel Flourent Héléne Brajard Julien |
| author_facet | Guillot Jules Koenig Guillaume Minbashian Kadi Frénod Emmanuel Flourent Héléne Brajard Julien |
| author_sort | Guillot Jules |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T03:32:14Z |
| format | Article |
| id | doaj.art-5abd7dbe232c45f8a3a77c866df6b33a |
| institution | Directory Open Access Journal |
| issn | 2267-3059 |
| language | English |
| last_indexed | 2024-04-11T03:32:14Z |
| publishDate | 2021-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ESAIM: Proceedings and Surveys |
| spelling | doaj.art-5abd7dbe232c45f8a3a77c866df6b33a2023-01-02T06:08:27ZengEDP SciencesESAIM: Proceedings and Surveys2267-30592021-01-017013714610.1051/proc/202107009proc2107009Partial differential equations for oceanic artificial intelligenceGuillot Jules0Koenig Guillaume1Minbashian Kadi2Frénod Emmanuel3Flourent Héléne4Brajard Julien5LMBA/UMR 6205, University of South BrittanyMIO, University of MarseilleNumerical Analysis and Scientific Computing, Department of Mathematics, Technical University of DarmstadtLMBA/UMR 6205, University of South Brittany and See-d, VannesLMBA/UMR 6205, University of South BrittanyLOCEAN, IPSLThe 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.https://www.esaim-proc.org/articles/proc/pdf/2021/01/proc2107009.pdfdata assimilationdata-model couplingartificial intelligencesstpde |
| spellingShingle | Guillot Jules Koenig Guillaume Minbashian Kadi Frénod Emmanuel Flourent Héléne Brajard Julien Partial differential equations for oceanic artificial intelligence ESAIM: Proceedings and Surveys data assimilation data-model coupling artificial intelligence sst pde |
| title | Partial differential equations for oceanic artificial intelligence |
| title_full | Partial differential equations for oceanic artificial intelligence |
| title_fullStr | Partial differential equations for oceanic artificial intelligence |
| title_full_unstemmed | Partial differential equations for oceanic artificial intelligence |
| title_short | Partial differential equations for oceanic artificial intelligence |
| title_sort | partial differential equations for oceanic artificial intelligence |
| topic | data assimilation data-model coupling artificial intelligence sst pde |
| url | https://www.esaim-proc.org/articles/proc/pdf/2021/01/proc2107009.pdf |
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