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Robust updating from experimental measurements in computational dynamics

Abstract : In general, deterministic computational models are used for updating a computational model using experiments. However, it is known that uncertainties have to be taken into account in order to improve the accuracy of the predictions, for instance in introducing a probabilistic model. Such an updating is called robust updating. Until now, most of the published works in this area concern the robust updating of dynamical systems in the low-frequency range with respect to data uncertainties using experiments, but model uncertainties are not taken into account. The present work proposes a methodology for robust updating of stochastic computational models in structural dynamics, for low- and mid-frequency ranges for which experiments are available. The stochastic computational model is constructed by the nonparametric probabilistic approach allowing model and data uncertainties to be taken into account. The cost function depends on the updating parameters made up of the mean parameters and the dispersion parameters of the probabilistic model.
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https://hal-upec-upem.archives-ouvertes.fr/hal-00698952
Contributor : Christian Soize <>
Submitted on : Friday, May 18, 2012 - 2:12:33 PM
Last modification on : Wednesday, February 26, 2020 - 7:06:08 PM

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  • HAL Id : hal-00698952, version 1

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Evangéline Capiez-Lernout, Christian Soize. Robust updating from experimental measurements in computational dynamics. USNCCM IX 2007, Ninth U. S. National Congress on Computational Mechanics, Jul 2007, San Francisco, United States. pp.2. ⟨hal-00698952⟩

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