Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
723648 | IFAC Proceedings Volumes | 2007 | 6 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Engineering
Computational Mechanics
Authors
Leyla Özkan, Reinout Romijn, Siep Weiland, Wolfgang Marquardt, Jobert Ludlage,