Stochastic representations and statistical inverse identification for uncertainty quantification in computational mechanics

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

Stochastic representations and statistical inverse identification for uncertainty quantification in computational mechanics

Christian Soize ^{1, *} Christophe Desceliers ¹ Johann Guilleminot ¹ Thinh-Tien Le ¹ Manh-Tu Nguyen ¹ Guillaume Perrin ^{1, 2, 3, 4} Jean-Marc Allain ⁵ H. Gharbi ⁵ Denis Duhamel ^{4, 3} Christine Funfschilling ²

* Corresponding author

1 MSME - Laboratoire de Modélisation et Simulation Multi Echelle

2 SNCF : Innovation & Recherche

3 NAVIER UMR 8205 - Laboratoire Navier

4 Dynamique des structures et identification

NAVIER UMR 8205 - Laboratoire Navier

5 LMS - Laboratoire de mécanique des solides

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.

Keywords : Polynomial Chaos Multiscale analysis Random fields Stochastic modeling Statistical inverse problems Uncertainty quantification

Document type :

Conference papers

Domain :

Engineering Sciences [physics] / Mechanics [physics.med-ph]

Mathematics [math] / Probability [math.PR]

Mathematics [math] / Statistics [math.ST]

Complete list of metadatas

Cited literature [63 references] Display Hide Download

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 : Thursday, July 18, 2019 - 4:36:06 PM
Long-term archiving on : Monday, April 24, 2017 - 7:40:55 PM

conference-2015-UNCECOMP-Crete...

Files produced by the author(s)

Stochastic representations and statistical inverse identification for uncertainty quantification in computational mechanics

File

Identifiers

Collections

Citation

Export

Metrics

1747

452

Contact

Stochastic representations and statistical inverse identification for uncertainty quantification in computational mechanics

File

Identifiers

Collections

Citation

Export

Share

Metrics

1747

452

Contact