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• W3204279182 abstract "This paper addresses variational data assimilation from a learning point of view. Data assimilation aims to reconstruct the time evolution of some state given a series of observations, possibly noisy and irregularly-sampled. Using automatic differentiation tools embedded in deep learning frameworks, we introduce end-to-end neural network architectures for data assimilation. It comprises two key components: a variational model and a gradient-based solver both implemented as neural networks. A key feature of the proposed end-to-end learning architecture is that we may train the NN models using both supervised and unsupervised strategies. Our numerical experiments on Lorenz-63 and Lorenz-96 systems report significant gain w.r.t. a classic gradient-based minimization of the variational cost both in terms of reconstruction performance and optimization complexity. Intriguingly, we also show that the variational models issued from the true Lorenz-63 and Lorenz-96 ODE representations may not lead to the best reconstruction performance. We believe these results may open new research avenues for the specification of assimilation models in geoscience." @default.
• W3204279182 created "2021-10-11" @default.
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• W3204279182 date "2021-10-01" @default.
• W3204279182 modified "2023-10-14" @default.
• W3204279182 title "Learning Variational Data Assimilation Models and Solvers" @default.
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• W3204279182 doi "https://doi.org/10.1029/2021ms002572" @default.
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