Bayesian posteriors of uncertainty quantification in computational structural dynamics for low- and medium-frequency ranges

Christian Soize (1)

1 MSME - Laboratoire de Modélisation et Simulation Multi Echelle

The paper is devoted to the modeling and identification of uncertainties in computational structural dynamics for low- and medium-frequency ranges. A complete methodology is presented for the identification procedure. The first eigenfrequencies are used to quantify the uncertainties in the low-frequency band while the frequency response functions are used to quantify the uncertainties in the medium-frequency band. The system-parameter uncertainties are taken into account with the parametric probabilistic approach. The model uncertainties are taken in to account with the nonparametric probabilistic approach. The posterior stochastic model of systemparameter uncertainties is identified using the Bayes method.

Mots clés: uncertainty quantification; nonparametric uncertainties; computational dynamics; medium frequency; identification; statistical inverse methods; prior stochastic model; posterior stochastic model; Bayes method; nonparametric statistics

Domaines: Mécanique [physics.med-ph]; Statistiques [math.ST]; Théorie [stat.TH]; Probabilités [math.PR]

Christian Soize. Bayesian posteriors of uncertainty quantification in computational structural dynamics for low- and medium-frequency ranges. Computers & Structures, 2013, 126 (-), pp.41-55. ⟨10.1016/j.compstruc.2013.03.020⟩. ⟨hal-00806363⟩