Statistics - Machine Learning; Physics - Atmospheric and Oceanic Physics
Abstract :
[en] Data assimilation addresses the problem of identifying plausible state
trajectories of dynamical systems given noisy or incomplete observations. In
geosciences, it presents challenges due to the high-dimensionality of
geophysical dynamical systems, often exceeding millions of dimensions. This
work assesses the scalability of score-based data assimilation (SDA), a novel
data assimilation method, in the context of such systems. We propose
modifications to the score network architecture aimed at significantly reducing
memory consumption and execution time. We demonstrate promising results for a
two-layer quasi-geostrophic model.
Disciplines :
Computer science
Author, co-author :
Rozet, François ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Big Data
Louppe, Gilles ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Big Data
Language :
English
Title :
Score-based Data Assimilation for a Two-Layer Quasi-Geostrophic Model
Publication date :
2023
Event name :
Machine Learning and the Physical Sciences Workshop (NeurIPS 2023)
Event place :
New Orleans, United States - Louisiana
Event date :
15-12-2023
Audience :
International
Peer reviewed :
Peer reviewed
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
