Skip to Main content Skip to Navigation
Conference papers

Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical Models

Redouane Lguensat 1 Julien Le Sommer 1 Sammy Metref 1 Emmanuel Cosme 1 Ronan Fablet 2, 3
2 Lab-STICC_IMTA_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Complete list of metadatas

Cited literature [26 references]  Display  Hide  Download

https://hal-imt-atlantique.archives-ouvertes.fr/hal-02366600
Contributor : Ronan Fablet <>
Submitted on : Saturday, November 16, 2019 - 8:50:32 AM
Last modification on : Thursday, November 19, 2020 - 1:01:31 PM
Long-term archiving on: : Monday, February 17, 2020 - 12:57:42 PM

File

NeurIPS_ML4PS_2019_109.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02366600, version 1

Citation

Redouane Lguensat, Julien Le Sommer, Sammy Metref, Emmanuel Cosme, Ronan Fablet. Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical Models. NeurIPS 2019 : 33rd Conference on Neural Information Processing Systems, Dec 2019, Vancouver, Canada. ⟨hal-02366600⟩

Share

Metrics

Record views

81

Files downloads

67