• Soize, Christian
• Nguyen, Manh-Tu
• Allain, Jean-Marc
• Perrin, Guillaume
• Le, Thinh-Tien
• Funfschilling, Christine
• Guilleminot, Johann
• Duhamel, Denis
• Gharbi, H.
• Desceliers, Christophe

Abstract

The paper deals with the statistical inverse problem for the identification of a non-Gaussian tensor-valued random field in high stochastic dimension. Such a random field can represent the parameter of a boundary value problem (BVP). The available experimental data, which correspond to observations, can be partial and limited. A general methodology and some algorithms are presented including some adapted stochastic representations for the non-Gaussian tensor-valued random fields and some ensembles of prior algebraic stochastic models for such random fields and the corresponding generators. Three illustrations are presented: (i) the stochastic modeling and the identification of track irregularities for dynamics of high-speed trains, (ii) a stochastic continuum modeling of random interphases from atomistic simulations for a polymer nanocomposite, and (iii) a multiscale experimental identification of the stochastic model of a heterogeneous random medium at mesoscale for mechanical characterization of a human cortical bone.

Topics

• nanocomposite
• polymer
• simulation
• random