Probabilistic models for computational stochastic mechanics and applications

Christian Soize (1)

1 LaM - Laboratoire de Mécanique

This paper deals with the validation and industrial applications of a nonparametric probabilistic approach of model uncertainties and data uncertainties in computational dynamics for linear and nonlinear dynamical systems, for complex structures and vibroacoustic systems. The concept of the nonparametric probababilistic approach for random uncertainties due to model errors and system-parameter uncertainties is introduced. A numerical validation proving the capability of the nonparametric probabilistic approach to take into account model uncertainties is presented. Then an experimental validation is given for the dynamics of a composite sandwich panel. Finally, four industrial applications of the nonparametric probabilistic modeling of random uncertainties in comptational stochastic mechanics for complex mechanical systems are presented.

Mots clés: uncertianty quantification; uncertain computational mechanics; nonparametric probabilistic method; model uncertainties; modeling errors; random matrix; structural dynamics; acoustic; vibrations; random uncertainties

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

Christian Soize. Probabilistic models for computational stochastic mechanics and applications. 9th International Conference on Structural Safety and Reliability ICOSSAR'05, Rome, Italy, 19--23 June 2005, Jun 2005, Rome, Italy. pp.23-42 (Plenary Lecture). ⟨hal-00687868⟩