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Learning ocean dynamical priors from noisy data using assimilation-derived neural nets

Abstract : Recent studies have investigated the identification of governing equations of geophysical systems from data. Here, we investigate such identification issues for ocean surface dynamcis from ocean remote sensing data. From a methodological point of view, we address the learning of data-driven dynamical models when only provided with a noisy training dataset. We propose a novel architecture that relies on data assimilation schemes to learn the underlying dynamical model through the minimization of a reconstruction cost. We demonstrate the relevance of the proposed architecture with respect to the state-of-the-art approaches in the identification and forecasting of synthetic and real case-studies.
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https://hal-imt-atlantique.archives-ouvertes.fr/hal-02285693
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Said Ouala, Duong Nguyen, Cédric Herzet, Lucas Drumetz, Bertrand Chapron, et al.. Learning ocean dynamical priors from noisy data using assimilation-derived neural nets. IGARSS 2019 - International Geoscience and remote Sensing Symposium, Jul 2019, Yokohama, Japan. pp.1-3, ⟨10.1109/IGARSS.2019.8900345⟩. ⟨hal-02285693⟩

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