Learning 4DVAR Inversion Directly From Observations - Laboratoire d'Informatique de Paris 6 Access content directly
Preprints, Working Papers, ... (Preprint) Year :

Learning 4DVAR Inversion Directly From Observations

Abstract

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.
Fichier principal
Vignette du fichier
main.pdf (533.95 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

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

Identifiers

  • HAL Id : hal-03863390 , version 1

Cite

Arthur Filoche, Julien Brajard, Anastase Charantonis, Dominique Béréziat. Learning 4DVAR Inversion Directly From Observations. 2022. ⟨hal-03863390v1⟩
109 View
77 Download

Share

Gmail Facebook Twitter LinkedIn More