Identification of the elasticity tensor of an uncertain biomechanical computational model using axial transmission

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des

This information is calculated in a fashion similar to (4), except that here only the directly predicted genes are taken into account. Directly predicted genes

In this framework E la, the vector space of elasticity tensors, is divided into eight conjugacy classes [17] which group tensors having conjugate symmetry groups.. The problem we

Experimental identification of a priori tensor-valued random field for the elasticity properties of cortical bones using in vivo ultrasonic measurements.. Joint conference of the

This type of model uncertainties are taken into account using the nonparametric probabilistic approach (see [4]) which consists in modeling the reduced mass and stiffness matrices

Thus, the purpose of this paper is the experimental identification and validation of the probabilistic model developed to take into account uncertainties in the elasticity tensor of

The robustness is introduced in taking into account (1) uncertainties in the simplified computational non-linear dynamical model used to carry out this identification and

Experimental identification of a prior tensor- valued random field for the elasticity properties of cortical bones using in vivo ultrasonic measurements.. Acoustics 2012, Apr