Sequential Multiscale Modeling Using Sparse Representation

作     者：Carlos J.Garcıa-Cervera Weiqing Ren Jianfeng Lu Weinan E 

作者机构：Mathematics DepartmentUniversity of CaliforniaSanta BarbaraCA 93106USA Courant Institute of Mathematical SciencesNew York UniversityNew YorkNY 10012USA Program in Applied and ComputationalMathematicsPrinceton UniversityPrincetonNJ 08544USA Department of Mathematics and PACMPrinceton UniversityPrincetonNJ 08544USA 

出 版 物：《Communications in Computational Physics》 (计算物理通讯（英文）)

年 卷 期：2008年第4卷第10期

页      面：1025-1033页

核心收录：

学科分类：07[理学] 0704[理学-天文学] 0701[理学-数学] 0702[理学-物理学] 070101[理学-基础数学] 

基　　金：The work of Carlos J.Garcıa-Cervera is supported in part by NSF grants DMS-0411504 and DMS-0505738 The work of Weiqing Ren is supported in part by NSF grant DMS-0604382 The work of Jianfeng Lu and Weinan E is supported in part by ONR grant N00014-01-0674,DOE grant DE-FG02-03ER25587 and NSF grant DMS-0407866 

主　　题：Multiscale modeling sparse grids 

摘      要：The main obstacle in sequential multiscale modeling is the pre-computation of the constitutive relationwhich often involvesmany independent *** constitutive relation of a polymeric fluid is a function of six variables,even after making the simplifying assumption that stress depends only on the rate of *** such a function is usually considered too *** the value of sequential multiscale modeling is often limited to“parameter passing.Here we demonstrate that sparse representations can be used to drastically reduce the computational cost for precomputing functions of many *** strategy dramatically increases the efficiency of sequential multiscale modeling,making it very competitive in many situations.