Skip to Main content Skip to Navigation
Journal articles

Maximum likelihood estimation of stochastic chaos representations from experimental data

Abstract : This paper deals with the identification of probabilistic models of the random coefficients in stochastic boundary value problems (SBVP). The data used in the identification correspond to measurements of the displacement field along the boundary of domains subjected to specified external forcing. Starting with a particular mathematical model for the mechanical behaviour of the specimen, the unknown field to be identified is projected on an adapted functional basis such as that provided by a finite element discretization. For each set of measurements of the displacement field along the boundary, an inverse problem is formulated to calculate the corresponding, optimal realization of the coefficients of the unknown random field on the adapted basis. Realizations of these coefficients are then used, in conjunction with the maximum likelihood principle, to set-up and solve an optimization problem for the estimation of the coefficients in a polynomial chaos representation of the parameters of the SBVP.
Complete list of metadatas

Cited literature [11 references]  Display  Hide  Download

https://hal-upec-upem.archives-ouvertes.fr/hal-00686154
Contributor : Christian Soize <>
Submitted on : Saturday, April 7, 2012 - 10:13:27 PM
Last modification on : Wednesday, February 26, 2020 - 7:06:08 PM
Long-term archiving on: : Monday, November 26, 2012 - 1:05:25 PM

File

publi-2006-IJNME-66_6_978-1001...
Files produced by the author(s)

Identifiers

Collections

Citation

Christophe Desceliers, R. Ghanem, Christian Soize. Maximum likelihood estimation of stochastic chaos representations from experimental data. International Journal for Numerical Methods in Engineering, Wiley, 2006, 66 (6), pp.978-1001. ⟨10.1002/nme.1576⟩. ⟨hal-00686154⟩

Share

Metrics

Record views

394

Files downloads

641