A Numerical Comparison Between Quasi-Monte Carlo and Sparse Grid Stochastic Collocation Methods
作者机构:CETEC-UFRBCentro44380-000Cruz das Almas-BABrazil.2 DMAT-UFPRCentro Politecnico81531-980Curitiba-PRBrazil
出 版 物:《Communications in Computational Physics》 (计算物理通讯(英文))
年 卷 期:2012年第12卷第9期
页 面:1051-1069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]
主 题:Karhunen-Loève expansion Monte Carlo quasi-Monte Carlo sparse grid
摘 要:Quasi-Monte Carlo methods and stochastic collocation methods based on sparse grids have become popular with solving stochastic partial differential *** methods use deterministic points for multi-dimensional integration or interpolation without suffering from the curse of *** is not evident which method is best,specially on random models of physical *** numerically study the error of quasi-Monte Carlo and sparse gridmethods in the context of groundwater flow in heterogeneous *** particular,we consider the dependence of the variance error on the stochastic dimension and the number of samples/collocation points for steady flow problems in which the hydraulic conductivity is a lognormal *** suitability of each technique is identified in terms of computational cost and error tolerance.