Machine learning with data assimilation and uncertainty quantification
for dynamical systems: a review
- URL: http://arxiv.org/abs/2303.10462v1
- Date: Sat, 18 Mar 2023 17:23:52 GMT
- Title: Machine learning with data assimilation and uncertainty quantification
for dynamical systems: a review
- Authors: Sibo Cheng, Cesar Quilodran-Casas, Said Ouala, Alban Farchi, Che Liu,
Pierre Tandeo, Ronan Fablet, Didier Lucor, Bertrand Iooss, Julien Brajard,
Dunhui Xiao, Tijana Janjic, Weiping Ding, Yike Guo, Alberto Carrassi, Marc
Bocquet, Rossella Arcucci
- Abstract summary: Recently, much effort has been given in combining DA, UQ and machine learning (ML) techniques.
These research efforts seek to address some critical challenges in high-dimensional dynamical systems.
This paper provides the first overview of the state-of-the-art researches in this interdisciplinary field.
- Score: 31.98982508378454
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively
used in analysing and reducing error propagation in high-dimensional
spatial-temporal dynamics. Typical applications span from computational fluid
dynamics (CFD) to geoscience and climate systems. Recently, much effort has
been given in combining DA, UQ and machine learning (ML) techniques. These
research efforts seek to address some critical challenges in high-dimensional
dynamical systems, including but not limited to dynamical system
identification, reduced order surrogate modelling, error covariance
specification and model error correction. A large number of developed
techniques and methodologies exhibit a broad applicability across numerous
domains, resulting in the necessity for a comprehensive guide. This paper
provides the first overview of the state-of-the-art researches in this
interdisciplinary field, covering a wide range of applications. This review
aims at ML scientists who attempt to apply DA and UQ techniques to improve the
accuracy and the interpretability of their models, but also at DA and UQ
experts who intend to integrate cutting-edge ML approaches to their systems.
Therefore, this article has a special focus on how ML methods can overcome the
existing limits of DA and UQ, and vice versa. Some exciting perspectives of
this rapidly developing research field are also discussed.
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