AN ADAPTIVE MULTIFIDELITY PC-BASED ENSEMBLE KALMAN INVERSION FOR INVERSE PROBLEMS

doi:10.1615/Int.J.UncertaintyQuantification.2019029059

	AN ADAPTIVE MULTIFIDELITY PC-BASED ENSEMBLE KALMAN INVERSION FOR INVERSE PROBLEMS
	Yan, Liang 1; Zhou, Tao2
	2019
发表期刊	INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION
ISSN	2152-5080
卷号	9 期号:3 页码:205-220
摘要	The ensemble Kalman inversion (EKI), as a derivative-free methodology, has been widely used in the parameter estimation of inverse problems. Unfortunately, its cost may become moderately large for systems described by high-dimensional nonlinear PDEs, as EKI requires a relatively large ensemble size to guarantee its performance. In this paper, we propose an adaptive multifidelity polynomial chaos (PC) based EKI technique to address this challenge. Our new strategy combines a large number of low-order PC surrogate model evaluations and a small number of high-fidelity forward model evaluations, yielding a multifidelity approach. Specifically, we present a new approach that adaptively constructs and refines a local multifidelity PC surrogate during the EKI simulation. Since the forward model evaluations are only required for updating the low-order local multifidelity PC model, whose number can be much smaller than the total ensemble size of the classic EKI, the entire computational costs are thus significantly reduced. The new algorithm was tested through the two-dimensional time fractional inverse diffuision problems and demonstrated great effectiveness in comparison with PC-based EKI and classic EKI.
关键词	Bayesian inverse problems ensemble Kalman inversion multifidelity polynomial chaos surrogate modeling
DOI	10.1615/Int.J.UncertaintyQuantification.2019029059
语种	英语
资助项目	NSF of China[11822111] ; NSF of China[11688101] ; NSF of China[91630203] ; NSF of China[11571351] ; NSF of China[11731006] ; NSF of China[11771081] ; Qing Lan project of Jiangsu Province ; Southeast University's Zhishan Young Scholars Program ; Science Challenge Project[TZ2018001] ; National Key Basic Research Program[2018YFB0704304] ; NCMIS ; Youth Innovation Promotion Association (CAS)
WOS研究方向	Engineering ; Mathematics
WOS类目	Engineering, Multidisciplinary ; Mathematics, Interdisciplinary Applications
WOS记录号	WOS:000478800200002
出版者	BEGELL HOUSE INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/35244
专题	计算数学与科学工程计算研究所
通讯作者	Zhou, Tao
作者单位	1.Southeast Univ, Dept Math, Nanjing 210096, Jiangsu, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, LSEC, Inst Computat Math & Sci Engn Comp, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yan, Liang,Zhou, Tao. AN ADAPTIVE MULTIFIDELITY PC-BASED ENSEMBLE KALMAN INVERSION FOR INVERSE PROBLEMS[J]. INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION,2019,9(3):205-220.
APA	Yan, Liang,&Zhou, Tao.(2019).AN ADAPTIVE MULTIFIDELITY PC-BASED ENSEMBLE KALMAN INVERSION FOR INVERSE PROBLEMS.INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION,9(3),205-220.
MLA	Yan, Liang,et al."AN ADAPTIVE MULTIFIDELITY PC-BASED ENSEMBLE KALMAN INVERSION FOR INVERSE PROBLEMS".INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION 9.3(2019):205-220.