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

Summary 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 provi...

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Bibliographic Details
Published in:International journal for numerical methods in engineering 2017-05, Vol.110 (5), p.440-466
Main Authors: Serafin, Kevin, Magnain, Benoît, Florentin, Eric, Parés, Núria, Díez, Pedro
Format: Article
Language:English
Subjects:
adaptivity
Aplicacions (Matemàtica)
Applications of mathematics
Assessments
Computation
Computer simulation
Cost analysis
goal-oriented error assessment
Matemàtiques i estadística
Mathematical analysis
Mathematical models
Mechanics
Models estocàstics
Physics
reduced basis
stochastic modeling
Stochastic models
Stochasticity
Strategy
Àrees temàtiques de la UPC
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Summary:Summary 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 © 2016 John Wiley & Sons, Ltd.
ISSN:0029-5981
1097-0207
DOI:10.1002/nme.5363