Domain-decomposed Bayesian inversion based on local Karhunen-Loève expansions

doi:10.1016/j.jcp.2024.112856

	Domain-decomposed Bayesian inversion based on local Karhunen-Loève expansions
	Xu, Zhihang1 ; Liao, Qifeng1 ; Li, Jinglai 2
	2024-05-01
发表期刊	JOURNAL OF COMPUTATIONAL PHYSICS (IF:3.8[JCR-2023],4.5[5-Year])
ISSN	0021-9991
EISSN	1090-2716
卷号	504
发表状态	已发表
DOI	10.1016/j.jcp.2024.112856
摘要	In many Bayesian inverse problems the goal is to recover a spatially varying random field. Such problems are often computationally challenging especially when the forward model is governed by complex partial differential equations (PDEs). The challenge is particularly severe when the spatial domain is large and the unknown random field needs to be represented by a high-dimensional parameter. In this paper, we present a domain-decomposed method to attack the dimensionality issue and the method decomposes the spatial domain and the parameter domain simultaneously. On each subdomain, a local Karhunen-Loève (KL) expansion is constructed, and a local inversion problem is solved independently in a parallel manner, and more importantly, in a lower-dimensional space. After local posterior samples are generated through conducting Markov chain Monte Carlo (MCMC) simulations on subdomains, a novel projection procedure is developed to effectively reconstruct the global field. In addition, the domain decomposition interface conditions are dealt with an adaptive Gaussian process-based fitting strategy. Numerical examples are provided to demonstrate the performance of the proposed method. © 2024 Elsevier Inc.
关键词	Bayesian networks Domain decomposition methods Inference engines Markov processes Monte Carlo methods Numerical methods Bayesian inference Bayesian inversion Domain decompositions Karhunen Loeve Expansion Local karhunen-loeve expansion Markov chain Monte Carlo Markov Chain Monte-Carlo Random fields Spatial domains Subdomain
URL	查看原文
收录类别	EI ; SCI
语种	英语
资助项目	National Natural Science Foundation of China[12071291] ; Science and Technology Commission of Shanghai Municipality, China[20JC1414300] ; Natural Science Foundation of Shanghai, China[20ZR1436200]
WOS研究方向	Computer Science ; Physics
WOS类目	Computer Science, Interdisciplinary Applications ; Physics, Mathematical
WOS记录号	WOS:001202510400001
出版者	Academic Press Inc.
EI入藏号	20240915644231
EI主题词	Inverse problems
EI分类号	723.4.1 Expert Systems ; 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory ; 921.6 Numerical Methods ; 922.1 Probability Theory ; 922.2 Mathematical Statistics
原始文献类型	Journal article (JA)
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/364647
专题	信息科学与技术学院信息科学与技术学院_PI研究组_廖奇峰组信息科学与技术学院_博士生
通讯作者	Liao, Qifeng
作者单位	1.School of Information Science and Technology, ShanghaiTech University, Shanghai; 201210, China 2.School of Mathematics, University of Birmingham, Birmingham; B15 2TT, United Kingdom
第一作者单位	信息科学与技术学院
通讯作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Xu, Zhihang,Liao, Qifeng,Li, Jinglai. Domain-decomposed Bayesian inversion based on local Karhunen-Loève expansions[J]. JOURNAL OF COMPUTATIONAL PHYSICS,2024,504.
APA	Xu, Zhihang,Liao, Qifeng,&Li, Jinglai.(2024).Domain-decomposed Bayesian inversion based on local Karhunen-Loève expansions.JOURNAL OF COMPUTATIONAL PHYSICS,504.
MLA	Xu, Zhihang,et al."Domain-decomposed Bayesian inversion based on local Karhunen-Loève expansions".JOURNAL OF COMPUTATIONAL PHYSICS 504(2024).