Identification of Bayesian posteriors for coefficients of chaos expansions

JOURNAL OF COMPUTATIONAL PHYSICS

卷:229

Article

Arnst, M.¹  Ghanem, R.¹  Soize, C.² 

[1] Univ So Calif, Los Angeles, CA 90089 USA

[2] Univ Paris Est, Lab Modelisat & Simulat Multiechelle, CNRS, MSME,UMR8208, F-77454 Champs Sur Marne 2, Marne La Vallee, France

关键词: Stochastic inversion;    Identification;    Bayesian;    Maximum likelihood;    Polynomial chaos;    Validation;    Uncertainty quantification;   

DOI  :  10.1016/j.jcp.2009.12.033

来源: Elsevier

【 摘 要 】

This article is concerned with the identification of probabilistic characterizations of random variables and fields from experimental data. The data used for the identification consist of measurements of several realizations of the uncertain quantities that must be characterized. The random variables and fields are approximated by a polynomial chaos expansion, and the coefficients of this expansion are viewed as unknown parameters to be identified. It is shown how the Bayesian paradigm can be applied to formulate and solve the inverse problem. The estimated polynomial chaos coefficients are hereby themselves characterized as random variables whose probability density function is the Bayesian posterior. This allows to quantify the impact of missing experimental information on the accuracy of the identified coefficients, as well as on subsequent predictions. An illustration in stochastic aeroelastic stability analysis is provided to demonstrate the proposed methodology. (C) 2009 Elsevier Inc. All rights reserved.

【 授权许可】

Free