|Title||Stochastic model and generator for random fields with symmetry properties: Application to the mesoscopic modeling of elastic random media|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||J Guilleminot, and C Soize|
|Journal||Multiscale Modeling & Simulation|
|Pagination||840 - 870|
This paper is concerned with the construction of a new class of generalized nonparametric probabilistic models for matrix-valued non-Gaussian random fields. More specifically, we consider the case where the random field may take its values in some subset of the set of real symmetric positive-definite matrices presenting sparsity and invariance with respect to given orthogonal transformations. Within the context of linear elasticity, this situation is typically faced in the multiscale analysis of heterogeneous microstructures, where the constitutive elasticity matrices may exhibit some material symmetry properties and may then belong to a given subset Msym n (R) of the set of symmetric positive-definite real matrices. First, we present an overall methodology relying on the framework of information theory and define a particular algebraic form for the random field. The representation involves two independent sources of uncertainties, namely, one preserving almost surely the topological structure in Msym n (R) and the other acting as a fully anisotropic stochastic germ. Such a parametrization does offer some flexibility for forward simulations and inverse identification by uncoupling the level of statistical fluctuations of the random field and the level of fluctuations associated with a stochastic measure of anisotropy. A novel numerical strategy for random generation is subsequently proposed and consists of solving a family of Ito stochastic differential equations. The algorithm turns out to be very efficient when the stochastic dimension increases and allows for the preservation of the statistical dependence between the components of the simulated random variables. A Sẗormer-Verlet algorithm is used for the discretization of the stochastic differential equation. The approach is finally exemplified by considering the class of almost isotropic random tensors. © Unauthorized reproduction of this article is prohibited.
|Short Title||Multiscale Modeling & Simulation|