CSpace  > 计算数学与科学工程计算研究所
AN ADAPTIVE MULTIFIDELITY PC-BASED ENSEMBLE KALMAN INVERSION FOR INVERSE PROBLEMS
Yan, Liang1; Zhou, Tao2
2019
Source PublicationINTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION
ISSN2152-5080
Volume9Issue:3Pages:205-220
AbstractThe 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.
KeywordBayesian inverse problems ensemble Kalman inversion multifidelity polynomial chaos surrogate modeling
DOI10.1615/Int.J.UncertaintyQuantification.2019029059
Language英语
Funding ProjectNSF 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 Research AreaEngineering ; Mathematics
WOS SubjectEngineering, Multidisciplinary ; Mathematics, Interdisciplinary Applications
WOS IDWOS:000478800200002
PublisherBEGELL HOUSE INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/35244
Collection计算数学与科学工程计算研究所
Corresponding AuthorZhou, Tao
Affiliation1.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
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Yan, Liang]'s Articles
[Zhou, Tao]'s Articles
Baidu academic
Similar articles in Baidu academic
[Yan, Liang]'s Articles
[Zhou, Tao]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Yan, Liang]'s Articles
[Zhou, Tao]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.