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Stochastic reduced order models for uncertain nonlinear dynamical systems

Abstract : A general methodology is presented for the consideration of both system parameters and model uncertainty in the determination of the response of geometrically nonlinear structural dynamic systems. The approach is rooted in the availability of reduced order models of these nonlinear systems with a deterministic basis extracted from a reference model (the mean model). Uncertainty, both from system parameters and model, is introduced by randomizing the coefficients of the reduced order model in a manner that guarantees the physical appropriateness of every realization of the reduced order model, i.e. while maintaining the fundamental properties of symmetry and positive definiteness of every such reduced order model. This randomization is achieved not by postulating a specific joint statistical distribution of the reduced order model coefficients but rather by deriving this distribution through the principle of maximization of the entropy constrained to satisfy the necessary symmetry and positive definiteness properties.
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https://hal-upec-upem.archives-ouvertes.fr/hal-00689708
Contributor : Christian Soize <>
Submitted on : Thursday, April 19, 2012 - 10:00:55 PM
Last modification on : Wednesday, February 26, 2020 - 7:06:08 PM
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  • HAL Id : hal-00689708, version 1

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M. P. Mignolet, Christian Soize. Stochastic reduced order models for uncertain nonlinear dynamical systems. IMAC XXV, 2007, Feb 2007, Orlando, Florida, United States. pp.1-28. ⟨hal-00689708⟩

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