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Pierre Tandeo
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Documents
Identifiants chercheurs
- pierre-tandeo
- 0000-0003-1647-8239
- Google Scholar : https://scholar.google.fr/citations?user=GpNNnJcAAAAJ&hl=fr&oi=ao
- IdRef : 148730337
Présentation
[https://tandeo.wordpress.com/](https://tandeo.wordpress.com/ "My research website")
[https://www.researchgate.net/](https://www.researchgate.net/ "My ResearchGate")
[https://github.com/ptandeo](https://github.com/ptandeo "My GitHub")
[https://tandeo.wordpress.com/](https://tandeo.wordpress.com/ "Mon site web de recherche")
[https://www.researchgate.net/](https://www.researchgate.net/ "Ma page ResearchGate")
[https://github.com/ptandeo](https://github.com/ptandeo "Ma page GitHub")
Publications
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Comparison of simulation-based algorithms for parameter estimation and state reconstruction in nonlinear state-space modelsDiscrete and Continuous Dynamical Systems - Series S, 2023, 16 (2), pp.240-264. ⟨10.3934/dcdss.2022054⟩
Article dans une revue
hal-03616079v1
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Analog data assimilation for the selection of suitable general circulation modelsGeoscientific Model Development, 2022, 15, pp.7203-7220. ⟨10.5194/gmd-15-7203-2022⟩
Article dans une revue
insu-03868833v2
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Combining analog method and ensemble data assimilation: application to the Lorenz-63 chaotic systemMachine Learning and Data Mining Approaches to Climate Science: proceedings of the 4th International Workshop on Climate Informatics, Springer, pp.3-12, 2015, 978-3-319-17219-4. ⟨10.1007/978-3-319-17220-0_1⟩
Chapitre d'ouvrage
hal-01202496v1
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Narrowing uncertainties in climate projections using data science tools3rd joint RIKEN & IMT-Atlantique workshop on Statistical Modeling and Machine Learning in Meteorology and Oceanography, Apr 2021, Brest, France
Communication dans un congrès
hal-03279029v1
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Selection of dynamic model using analog data assimilationStatistical Modeling and Machine Learning in Meteorology and Oceanography, Feb 2020, Brest, France
Communication dans un congrès
hal-02921295v1
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Selection of dynamical model using analog data assimilationClimate Informatics 2020, Sep 2020, Oxford, United Kingdom
Communication dans un congrès
hal-02927329v1
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An efficient particle-based method for maximum likelihood estimation in nonlinear state-space models2018
Pré-publication, Document de travail
hal-01708631v3
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