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Conference papers
Stochastic representations and statistical inverse identification for uncertainty quantification in computational mechanics
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.
Cited literature [63 references]
https://hal-upec-upem.archives-ouvertes.fr/hal-01158252
Contributor : Christian Soize <>
Submitted on : Saturday, May 30, 2015 - 5:54:07 PM
Last modification on : Saturday, October 10, 2020 - 3:35:54 AM
Long-term archiving on: : Monday, April 24, 2017 - 7:40:55 PM
Contributor : Christian Soize <>
Submitted on : Saturday, May 30, 2015 - 5:54:07 PM
Last modification on : Saturday, October 10, 2020 - 3:35:54 AM
Long-term archiving on: : Monday, April 24, 2017 - 7:40:55 PM
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