2010
DOI: 10.1002/nme.2865
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eXtended Stochastic Finite Element Method for the numerical simulation of heterogeneous materials with random material interfaces

Abstract: SUMMARYAn eXtended Stochastic Finite Element Method has been recently proposed for the numerical solution of partial differential equations defined on random domains. This method is based on a marriage between the eXtended Finite Element Method and spectral stochastic methods. In this article, we propose an extension of this method for the numerical simulation of random multi-phased materials. The random geometry of material interfaces is described implicitly by using random level set functions. A fixed determ… Show more

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Cited by 49 publications
(31 citation statements)
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“…To avoid the former drawback, one possibility is to introduce additional functions (enrichment basis method) that can account for these discontinuities. This technique has been proposed for the stochastic finite element method in [22,23]. Another possibility consists in applying the transformation method proposed in [24,25] which will be used in the following.…”
Section: A Parametric Finite Element Modelmentioning
confidence: 99%
“…To avoid the former drawback, one possibility is to introduce additional functions (enrichment basis method) that can account for these discontinuities. This technique has been proposed for the stochastic finite element method in [22,23]. Another possibility consists in applying the transformation method proposed in [24,25] which will be used in the following.…”
Section: A Parametric Finite Element Modelmentioning
confidence: 99%
“…The most natural way to account for randomness on the geometry consist in remeshing according to the deformation but the remeshing leads to a discontinuous solution in the space of the input parameters and can create additional numerical noise which can disturb the random solution. Alternatives have been proposed in the literature [5][6][7][8][9] to avoid remeshing. In the following, we will focus mainly on uncertainties on the behaviour laws.…”
Section: Stochastic Problemmentioning
confidence: 99%
“…To solve (9), sampling techniques, like the Monte Carlo Simulation Methods (MCSM) [12] [11], or perturbation methods [44] [45] can be applied. In this paper, we will focus only on the approximation methods which are well fitted to solve (9) when the entries of the vector A(p) are smooth functions of the input parameters p.…”
Section: Stochastic Problemmentioning
confidence: 99%
“…However, taking into account a high number of random parameters is of first importance in a stochastic multiscale analysis and we thus propose a different methodology based on polynomial chaos representations. Initiated in [14], the methodology to construct a polynomial chaos expansion of random fields has been intensely developed to solve stochastic partial differential equations [3,15,16,13,24,22,29,32,31,35,38,11] but also for the identification of random fields using experimental data and classical inference techniques [17,2] or maximum likelihood estimation [9,10,43,18]. A new methodology has been recently introduced to deal with the identification of polynomial chaos representations in high-dimension [39,41].…”
Section: Introductionmentioning
confidence: 99%