Enhanced goal-oriented error assessment and computational strategies in adaptive reduced basis solver for stochastic problems

被引：4

作者：

Serafin, Kevin ^{[1
]}

Magnain, Benoit ^{[1
]}

Florentin, Eric ^{[1
]}

Pares, Nuria ^{[2
]}

Diez, Pedro ^{[2
]}

机构：

[1] Univ Orleans, CVL, INSA, PRISME, 88 Blvd Lahitolle, F-18020 Bourges, France

[2] Univ Politecn Cataluna, Lab Calcul Numer LaCaN, Campus Nord UPC, E-08034 Barcelona, Spain

来源：

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING | 2017年 / 110卷 / 05期

关键词：

reduced basis; adaptivity; stochastic modeling; goal-oriented error assessment; LINEAR-FUNCTIONAL OUTPUTS; SIMULATION; EQUATIONS; BOUNDS; MODELS;

D O I：

10.1002/nme.5363

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This work focuses on providing accurate low-cost approximations of stochastic finite elements simulations in the framework of linear elasticity. In a previous work, an adaptive strategy was introduced as an improved Monte-Carlo method for multi-dimensional large stochastic problems. We provide here a complete analysis of the method including a new enhanced goal-oriented error estimator and estimates of CPU (computational processing unit) cost gain. Technical insights of these two topics are presented in details, and numerical examples show the interest of these new developments. Copyright (c) 2016 John Wiley & Sons, Ltd.

引用

页码：440 / 466

页数：27

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