Efficient non-linear model reduction via a least-squares Petrov-Galerkin projection and compressive tensor approximations

A Petrov–Galerkin projection method is proposed for reducing the dimension of a discrete non‐linear static or dynamic computational model in view of enabling its processing in real time. The right reduced‐order basis is chosen to be invariant and is constructed using the Proper Orthogonal Decomposit...

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Bibliographic Details
Published in:	International journal for numerical methods in engineering Vol. 86; no. 2; pp. 155 - 181
Main Authors:	Carlberg, Kevin, Bou-Mosleh, Charbel, Farhat, Charbel
Format:	Journal Article
Language:	English
Published:	Chichester, UK John Wiley & Sons, Ltd 15.04.2011 Wiley
Subjects:	Approximation compressive approximation Computation Computational efficiency discrete non-linear systems Exact sciences and technology Fluid dynamics Fundamental areas of phenomenology (including applications) gappy data Invariants Mathematical models Mathematics Methods of scientific computing (including symbolic computation, algebraic computation) non-linear model reduction Nonlinear dynamics Nonlinearity Numerical analysis. Scientific computation Petrov-Galerkin projection Physics Projection proper orthogonal decomposition Sciences and techniques of general use Solid mechanics Structural and continuum mechanics Turbulent flows, convection, and heat transfer Vibration, mechanical wave, dynamic stability (aeroelasticity, vibration control...) Invariant Turbulent flow Karhunen Loeve transformation Vibration compressive approximation Gauss Newton method Real time Modeling gappy data Galerkin-Petrov method discrete non-linear systems Static model Petrov―Galerkin projection Least squares method Galerkin method Reduced order systems proper orthogonal decomposition Non linear effect Incomplete information Dynamic model non-linear model reduction
ISSN:	0029-5981, 1097-0207, 1097-0207
Online Access:	Get full text
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