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Communication Dans Un Congrès Année : 2023

Learning 4DVAR inversion directly from observations

Résumé

Variational data assimilation and deep learning share many algorithmic aspects in common. While the former focuses on system state estimation, the latter provides great inductive biases to learn complex relationships. We here design a hybrid architecture learning the assimilation task directly from partial and noisy observations, using the mechanistic constraint of the 4DVAR algorithm. Finally, we show in an experiment that the proposed method was able to learn the desired inversion with interesting regularizing properties and that it also has computational interests.
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Dates et versions

hal-03863390 , version 1 (21-11-2022)
hal-03863390 , version 2 (05-04-2023)

Identifiants

  • HAL Id : hal-03863390 , version 2

Citer

Arthur Filoche, Julien Brajard, Anastase Charantonis, Dominique Béréziat. Learning 4DVAR inversion directly from observations. Workshop on Machine Learning and Data Assimilation for Dynamical Systems (MLDADS), International Conference on Computational Science (ICCS), Jul 2023, Prague, Czech Republic. ⟨hal-03863390v2⟩
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