Parallel ensemble Kalman method with total variation regularization for large-scale field inversion

doi:10.1016/j.jcp.2024.113059

CORC > 力学研究所 > 中国科学院力学研究所 > 非线性力学国家重点实验室

	Parallel ensemble Kalman method with total variation regularization for large-scale field inversion
	Zhang XL(张鑫磊); Zhang L(张磊); He GW(何国威)
刊名	JOURNAL OF COMPUTATIONAL PHYSICS
	2024-07-15
卷号	509 页码:18
关键词	Ensemble Kalman method Parallel implementation Field inversion Data assimilation Machine learning
ISSN号	0021-9991
DOI	10.1016/j.jcp.2024.113059
通讯作者	He, Guowei(hgw@lnm.imech.ac.cn)
英文摘要	Field inversion is often encountered in data -driven computational modeling to infer latent spatial- varying parameters from available observations. The ensemble Kalman method is emerging as a useful tool for solving field inversion problems due to its derivative -free merits. However, the method is computationally prohibitive for large-scale field inversion with high -dimensional observation data, which necessitates developing a practical efficient implementation strategy. In this work, we propose a parallel implementation of the ensemble Kalman method with total variation regularization for large-scale field inversion problems. It is achieved by partitioning the computational domain into non -overlapping subdomains and performing local ensemble Kalman updates at each subdomain parallelly. In doing so, the computational complexity of the ensemblebased inversion method is significantly reduced to the level of local subdomains. Further, the total variation regularization is employed to smoothen the physical field over the entire domain, which can reduce the inference discrepancy caused by missing covariances near subdomain interfaces. The capability of the proposed method is demonstrated in three field inversion problems with increasing complexity, i.e., the diffusion problem, the scalar transport problem and the Reynolds averaged Navier-Stokes closure problem. The numerical results show that the proposed method can significantly improve computational efficiency with satisfactory inference accuracy.
分类号	一类/力学重要期刊
资助项目	NSFC Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics[11988102] ; National Natural Science Foundation of China[12102435] ; China Post-doctoral Science Foundation[2021M690154] ; Young Elite Scientists Sponsorship Program by CAST[2022QNRC001]
WOS关键词	DATA-DRIVEN
WOS研究方向	Computer Science ; Physics
语种	英语
WOS记录号	WOS:001239659300001
资助机构	NSFC Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics ; National Natural Science Foundation of China ; China Post-doctoral Science Foundation ; Young Elite Scientists Sponsorship Program by CAST
其他责任者	He, Guowei
内容类型	期刊论文
源URL	[http://dspace.imech.ac.cn/handle/311007/95572]
专题	力学研究所_非线性力学国家重点实验室
推荐引用方式 GB/T 7714	Zhang XL,Zhang L,He GW. Parallel ensemble Kalman method with total variation regularization for large-scale field inversion[J]. JOURNAL OF COMPUTATIONAL PHYSICS,2024,509:18.
APA	张鑫磊,张磊,&何国威.(2024).Parallel ensemble Kalman method with total variation regularization for large-scale field inversion.JOURNAL OF COMPUTATIONAL PHYSICS,509,18.
MLA	张鑫磊,et al."Parallel ensemble Kalman method with total variation regularization for large-scale field inversion".JOURNAL OF COMPUTATIONAL PHYSICS 509(2024):18.