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Article
Nonlinear Model Reduction Using Group Proper Orthogonal Decomposition
International Journal of Numerical Analysis and Modeling
  • Benjamin T. Dickinson
  • John R. Singler, Missouri University of Science and Technology
Abstract
We propose a new method to reduce the cost of computing nonlinear terms in projec- tion based reduced order models with global basis functions. We develop this method by extending ideas from the group nite element (GFE) method to proper orthogonal decomposition (POD) and call it the group POD method. Here, a scalar two-dimensional Burgers' equation is used as a model problem for the group POD method. Numerical results show that group POD models of Burgers' equation are as accurate and are computationally more e cient than standard POD models of Burgers' equation.
Department(s)
Mathematics and Statistics
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2010 Institute for Scientific Computing and Information, All rights reserved.
Publication Date
1-1-2010
Citation Information
Benjamin T. Dickinson and John R. Singler. "Nonlinear Model Reduction Using Group Proper Orthogonal Decomposition" International Journal of Numerical Analysis and Modeling Vol. 7 Iss. 2 (2010) p. 356 - 372
Available at: http://works.bepress.com/john-singler/33/