Article (Scientific journals)
Maximum entropy approach to the identification of stochastic reduced-order models of nonlinear dynamical systems
Arnst, Maarten; Ghanem, Roger; Masri, Sami
2010In Aeronautical Journal, 114 (1160), p. 637-650
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Abstract :
[en] Data-driven methodologies based on the restoring force method have been developed over the past few decades for building predictive reduced-order models (ROMs) of nonlinear dynamical systems. These methodologies involve fitting a polynomial expansion of the restoring force in the dominant state variables to observed states of the system. ROMs obtained in this way are usually prone to errors and uncertainties due to the approximate nature of the polynomial expansion and experimental limitations. We develop in this article a stochastic methodology that endows these errors and uncertainties with a probabilistic structure in order to obtain a quantitative description of the proximity between the ROM and the system that it purports to represent. Specifically, we propose an entropy maximization procedure for constructing a multi-variate probability distribution for the coefficients of power-series expansions of restoring forces. An illustration in stochastic aeroelastic stability analysis is provided to demonstrate the proposed framework.
Disciplines :
Mechanical engineering
Author, co-author :
Arnst, Maarten ;  University of Southern California > Department of Civil and Environmental Engineering
Ghanem, Roger;  University of Southern California > Department of Civil and Environmental Engineering
Masri, Sami;  University of Southern California > Department of Civil and Environmental Engineering
Language :
English
Title :
Maximum entropy approach to the identification of stochastic reduced-order models of nonlinear dynamical systems
Publication date :
October 2010
Journal title :
Aeronautical Journal
ISSN :
0001-9240
Publisher :
Royal Aeronautical Society
Volume :
114
Issue :
1160
Pages :
637-650
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 15 November 2011

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