ModelReduction.com

Nonlinear Model Reduction

Nonlinear model reduction in the general case is a challenge. The most popular method is Proper Orthogonal Decomposition. It can be generalized by means of empirical gramians. There is a generalization of balancing for a nonlinear model.An interesting new approach is trajectory piecewise model reduction. Links below will give you some introduction to these methods.

Reviews

D. J. Lucia, P. S. Beran, and W. A. Silva,
Reduced-order modeling: new approaches for computational physics.
Progress in Aerospace Sciences, vol. 40, N 1/2, pp. 51-117, 2004.
Paper at ScienceDirect.

External Links

Theses

Patricia Astrid,
Reduction of process simulation models : a proper orthogonal decomposition approach

J.A. Atwell,
Proper Orthogonal Decomposition for Reduced Order Control of Partial Differential Equations

Andrew J. Newman,
Modeling and Reduction with Applications to Semiconductor Processing

Michal Rewienski,
A trajectory Piecewise-Linear Approach to Model Order Reduction of Nonlinear Dynamical Systems

Jacquelien M.A. Scherpen,
Balancing for nonlinear systems

Homepages

Michael Hinze

Belinda B. King

Michael Navon

Jacquelien M.A. Scherpen

Stefan Volkwein

Sanjay Lall

Andrew Newman