Two-level Space-time Domain Decomposition Methods for Three-dimensional Unsteady Inverse Source Problems

	Two-level Space-time Domain Decomposition Methods for Three-dimensional Unsteady Inverse Source Problems
	X. Deng; X.-C. Cai; J. Zou
刊名	Journal of Scientific Computing
	2016
英文摘要	As the number of processor cores on supercomputers becomes larger and larger, algorithms with high degree of parallelism attract more attention. In this work, we propose a two-level space–time domain decomposition method for solving an inverse source prob- lem associated with the time-dependent convection–diffusion equation in three dimensions. We introduce a mixed finite element/finite difference method and a one-level and a two-level space–timeparalleldomaindecompositionpreconditionerfortheKarush–Kuhn–Tuckersys- tem induced from reformulating the inverse problem as an output least-squares optimization problem in the entire space-time domain. The new full space–time approach eliminates the sequential steps in the optimization outer loop and the inner forward and backward time marching processes, thus achieves high degree of parallelism. Numerical experiments vali- date that this approach is effective and robust for recovering unsteady moving sources. We will present strong scalability results obtained on a supercomputer with more than 1000 processors.
收录类别	SCI
原文出处	http://link.springer.com/article/10.1007%2Fs10915-015-0109-1)
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/10282]
专题	深圳先进技术研究院_数字所
作者单位	Journal of Scientific Computing
推荐引用方式 GB/T 7714	X. Deng,X.-C. Cai,J. Zou. Two-level Space-time Domain Decomposition Methods for Three-dimensional Unsteady Inverse Source Problems[J]. Journal of Scientific Computing,2016.
APA	X. Deng,X.-C. Cai,&J. Zou.(2016).Two-level Space-time Domain Decomposition Methods for Three-dimensional Unsteady Inverse Source Problems.Journal of Scientific Computing.
MLA	X. Deng,et al."Two-level Space-time Domain Decomposition Methods for Three-dimensional Unsteady Inverse Source Problems".Journal of Scientific Computing (2016).