Abonnement à la biblothèque: Guest
Portail numérique Bibliothèque numérique eBooks Revues Références et comptes rendus Collections
International Journal for Uncertainty Quantification
Facteur d'impact: 3.259 Facteur d'impact sur 5 ans: 2.547 SJR: 0.417 SNIP: 0.8 CiteScore™: 1.52

ISSN Imprimer: 2152-5080
ISSN En ligne: 2152-5099

Ouvrir l'accès

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2017019428
pages 189-205

A FULLY ADAPTIVE INTERPOLATED STOCHASTIC SAMPLING METHOD FOR LINEAR RANDOM PDES

Felix Anker
Weierstraß-Institut für Angewandte Analysis und Stochastik, Berlin, Germany
Christian Bayer
Weierstraß-Institut für Angewandte Analysis und Stochastik, Berlin, Germany
Martin Eigel
Weierstraß-Institut für Angewandte Analysis und Stochastik, Berlin, Germany
Johannes Neumann
Weierstraß-Institut für Angewandte Analysis und Stochastik, Berlin, Germany
John Schoenmakers
Weierstraß-Institut für Angewandte Analysis und Stochastik, Berlin, Germany

RÉSUMÉ

A numerical method for the fully adaptive sampling and interpolation of linear PDEs with random data is presented. It is based on the idea that the solution of the PDE with stochastic data can be represented as conditional expectation of a functional of a corresponding stochastic differential equation (SDE). The spatial domain is decomposed by a nonuniform grid and a classical Euler scheme is employed to approximately solve the SDE at grid vertices. Interpolation with a conforming finite element basis is employed to reconstruct a global solution of the problem. An a posteriori error estimator is introduced which provides a measure of the different error contributions. This facilitates the formulation of an adaptive algorithm to control the overall error by either reducing the stochastic error by locally evaluating more samples, or the approximation error by locally refining the underlying mesh. Numerical examples illustrate the performance of the presented novel method.


Articles with similar content:

AN EFFICIENT MESH-FREE IMPLICIT FILTER FOR NONLINEAR FILTERING PROBLEMS
International Journal for Uncertainty Quantification, Vol.6, 2016, issue 1
Feng Bao, Yanzhao Cao, Guannan Zhang, Clayton G. Webster
ANALYSIS OF VARIANCE-BASED MIXED MULTISCALE FINITE ELEMENT METHOD AND APPLICATIONS IN STOCHASTIC TWO-PHASE FLOWS
International Journal for Uncertainty Quantification, Vol.4, 2014, issue 6
Guang Lin, Yalchin Efendiev, Lijian Jiang, Jia Wei
AN OVERVIEW OF INVERSE MATERIAL IDENTIFICATION WITHIN THE FRAMEWORKS OF DETERMINISTIC AND STOCHASTIC PARAMETER ESTIMATION
International Journal for Uncertainty Quantification, Vol.3, 2013, issue 4
Miguel A. Aguilo, Laura P. Swiler, Angel Urbina
A GRADIENT-BASED SAMPLING APPROACH FOR DIMENSION REDUCTION OF PARTIAL DIFFERENTIAL EQUATIONS WITH STOCHASTIC COEFFICIENTS
International Journal for Uncertainty Quantification, Vol.5, 2015, issue 1
Miroslav Stoyanov, Clayton G. Webster
PARTICLE-FILTER BASED UPSCALING FOR TURBULENT REACTING FLOW SIMULATIONS
International Journal for Multiscale Computational Engineering, Vol.15, 2017, issue 1
Tarek Echekki, Shubham Srivastava