Article ID Journal Published Year Pages File Type
723648 IFAC Proceedings Volumes 2007 6 Pages PDF
Abstract

We present a novel model reduction methodology for the approximation of large-scale nonlinear systems. The methodology stems from the need to find computationally efficient substitute models for nonlinear systems. The nonlinear system is viewed as a grey-box model with a mechanistic (first-principle) component and an empirical (black-box) component identified for the computationally intensive parts of the nonlinear system. The mechanistic part is approximated using proper orthogonal decompositions whereas the empirical part is identified as polynomial functions by parameter estimation using the reduced order mechanistic part.

Related Topics
Physical Sciences and Engineering Engineering Computational Mechanics
Authors
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