The full documentation of the package available at : https://cia-oceanix.github.io/ocean4dvarnet/
4DVarNet (short for 4D-Variational Network) is a deep learning-based framework that combines ideas from data assimilation (specifically 4D-Var) with neural networks to reconstruct high-resolution spatiotemporal data from incomplete and noisy observations, commonly applied in Earth sciences like oceanography and meteorology.
Get more informations about Ocean4DVarnet Concepts.
To install the package, you can use the following command:
pip install ocean4dvarnetGet more information in the installing section
## Usage
import ocean4dvarnetProject getting started : https://github.com/CIA-Oceanix/4dvarnet-starter
This package contains the model's main functions.
Another package is available, containing extended functionality : models, dataloader, etc. ... : Ocean4DVarnet-contrib : https://github.com/CIA-Oceanix/ocean4dvarnet-contrib
If you want to add specific models, data loaders, utilities, etc., the best way to do so is to contribute to the Ocean4DVarnet-contrib project.
If you want to contribute to the core Ocean4DVarnet Package, please follow the informations in the contributing section.
- Project getting started : https://github.com/CIA-Oceanix/4dvarnet-starter
- 4DVarNet papers:
- Fablet, R.; Amar, M. M.; Febvre, Q.; Beauchamp, M.; Chapron, B. END-TO-END PHYSICS-INFORMED REPRESENTATION LEARNING FOR SA℡LITE OCEAN REMOTE SENSING DATA: APPLICATIONS TO SA℡LITE ALTIMETRY AND SEA SURFACE CURRENTS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2021, V-3–2021, 295–302. https://doi.org/10.5194/isprs-annals-v-3-2021-295-2021.
- Fablet, R.; Chapron, B.; Drumetz, L.; Mmin, E.; Pannekoucke, O.; Rousseau, F. Learning Variational Data Assimilation Models and Solvers. Journal of Advances in Modeling Earth Systems n/a (n/a), e2021MS002572. https://doi.org/10.1029/2021MS002572.
- Fablet, R.; Beauchamp, M.; Drumetz, L.; Rousseau, F. Joint Interpolation and Representation Learning for Irregularly Sampled Satellite-Derived Geophysical Fields. Frontiers in Applied Mathematics and Statistics 2021, 7. https://doi.org/10.3389/fams.2021.655224.
Copyright IMT Atlantique/OceaniX, contributor(s) : M. Beauchamp, R. Fablet, Q. Febvre, D. Zhu (IMT Atlantique)
Contact person: ronan.fablet@imt-atlantique.fr.
This software is a computer program whose purpose is to apply deep learning schemes to dynamical systems and ocean remote sensing data.
This software is governed by the CeCILL-C license under French law and abiding by the rules of distribution of free software.
You can use, modify and/ or redistribute the software under the terms of the CeCILL-C license as circulated by CEA, CNRS and INRIA at the following URL "http://www.cecill.info". As a counterpart to the access to the source code and rights to copy, modify and redistribute granted by the license, users are provided only with a limited warranty and the software's author, the holder of the economic rights, and the successive licensors have only limited liability.
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