A novel model reduction technique for static systems is presented. The method is developed using a goal-oriented framework, and it extends the concept of snapshots for proper orthogonal decomposition (POD) to include (sensitivity) derivatives of the state with respect to system input parameters. The resulting reduced-order model generates accurate approximations due to its goal-oriented construction and the explicit 'training' of the model for parameter changes. The model is less computationally expensive to construct than typical POD approaches, since efficient multiple right-hand side solvers can be used to compute the sensitivity derivatives. The effectiveness of the method is demonstrated on a parameterized aerospace structure problem. © 2010 John Wiley & Sons, Ltd.
|Original language||English (US)|
|Number of pages||22|
|Journal||International Journal for Numerical Methods in Engineering|
|State||Published - Dec 10 2010|