CSpace  > 计算数学与科学工程计算研究所
Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems
Yan, Liang1; Zhou, Tao2
2019-03-15
Source PublicationJOURNAL OF COMPUTATIONAL PHYSICS
ISSN0021-9991
Volume381Pages:110-128
AbstractThe polynomial chaos (PC) expansion has been widely used as a surrogate model in the Bayesian inference to speed up the Markov chain Monte Carlo (MCMC) calculations. However, the use of a PC surrogate introduces the modeling error, that may severely distort the estimate of the posterior distribution. This error can be corrected by increasing the order of the PC expansion, but the cost for building the surrogate may increase dramatically. In this work, we seek to address this challenge by proposing an adaptive procedure to construct a multi-fidelity PC surrogate. This new strategy combines (a large number of) low-fidelity surrogate model evaluations and (a small number of) high-fidelity model evaluations, yielding a locally adaptive multi-fidelity approach. Here the low-fidelity surrogate is chosen as the prior-based PC surrogate, while the high-fidelity model refers to the true forward model. The key idea is to construct and refine the multi-fidelity approach over a sequence of samples adaptively determined from data so that the approximation can eventually concentrate on the posterior distribution. We illustrate the performance of the proposed strategy through two nonlinear inverse problems. It is shown that the proposed adaptive multi-fidelity approach can improve significantly the accuracy, yet without a dramatic increase in computational complexity. The numerical results also indicate that our new algorithm can enhance the efficiency by several orders of magnitude compared to a standard MCMC approach using only the true forward model. (C) 2019 Elsevier Inc. All rights reserved.
KeywordBayesian inverse problems Multi-fidelity polynomial chaos Surrogate modeling Markov chain Monte Carlo
DOI10.1016/j.jcp.2018.12.025
Language英语
Funding ProjectNSFC[11822111] ; NSFC[11688101] ; NSFC[91630203] ; NSFC[11571351] ; NSFC[11731006] ; NSFC[11771081] ; Qing Lan project of Jiangsu Province ; Southeast UniversityZhishan Young Scholars Program ; science challenge project[TZ2018001] ; NCMIS ; youth innovation promotion association (CAS)
WOS Research AreaComputer Science ; Physics
WOS SubjectComputer Science, Interdisciplinary Applications ; Physics, Mathematical
WOS IDWOS:000458147100007
PublisherACADEMIC PRESS INC ELSEVIER SCIENCE
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/32447
Collection计算数学与科学工程计算研究所
Affiliation1.Southeast Univ, Dept Math, Nanjing 210096, Jiangsu, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Inst Computat Math, LSEC, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Yan, Liang,Zhou, Tao. Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems[J]. JOURNAL OF COMPUTATIONAL PHYSICS,2019,381:110-128.
APA Yan, Liang,&Zhou, Tao.(2019).Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems.JOURNAL OF COMPUTATIONAL PHYSICS,381,110-128.
MLA Yan, Liang,et al."Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems".JOURNAL OF COMPUTATIONAL PHYSICS 381(2019):110-128.
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.