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Probabilistic model identification of uncertainties in computational models for dynamical systems and experimental validation

Abstract : We present a methodology to perform the identification and validation of complex uncertain dynamical systems using experimental data, for which uncertainties are taken into account by using the nonparametric probabilistic approach. Such a probabilistic model of uncertainties allows both model uncertainties and parameter uncertainties to be addressed by using only a small number of unknown identification parameters. Consequently, the optimization problem which has to be solved in order to identify the unknown identification parameters from experiments is feasible. Two formulations are proposed. The first one is the mean-square method for which a usual differentiable objective function and an unusual non-differentiable objective function are proposed. The second one is the maximum likelihood method coupling with a statistical reduction which leads us to a considerable improvement of the method. Three applications with experimental validations are presented in the area of structural vibrations and vibroacoustics.
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Christian Soize, Evangéline Capiez-Lernout, J.-F. Durand, C. Fernandez, L. Gagliardini. Probabilistic model identification of uncertainties in computational models for dynamical systems and experimental validation. Computer Methods in Applied Mechanics and Engineering, Elsevier, 2008, 198 (1), pp.150-163. ⟨10.1016/j.cma.2008.04.007⟩. ⟨hal-00686138⟩

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