Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review

doi:10.1109/JAS.2023.123537

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > IEEE/CAA Journal of Automatica Sinica

	Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review
	Sibo Cheng; César Quilodrán-Casas; Said Ouala; Alban Farchi; Che Liu; Pierre Tandeo; Ronan Fablet; Didier Lucor; Bertrand Iooss; Julien Brajard
刊名	IEEE/CAA Journal of Automatica Sinica
	2023
卷号	10 期号:6 页码:1361-1387
关键词	Data assimilation (DA) deep learning machine learning (ML) reduced-order-modelling uncertainty quantification (UQ)
ISSN号	2329-9266
DOI	10.1109/JAS.2023.123537
英文摘要	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 state-of-the-art researches in this interdisciplinary field, covering a wide range of applications. This review is aimed 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.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/51676]
专题	自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Sibo Cheng,César Quilodrán-Casas,Said Ouala,et al. Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review[J]. IEEE/CAA Journal of Automatica Sinica,2023,10(6):1361-1387.
APA	Sibo Cheng.,César Quilodrán-Casas.,Said Ouala.,Alban Farchi.,Che Liu.,...&Rossella Arcucci.(2023).Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review.IEEE/CAA Journal of Automatica Sinica,10(6),1361-1387.
MLA	Sibo Cheng,et al."Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review".IEEE/CAA Journal of Automatica Sinica 10.6(2023):1361-1387.