Article ID: | iaor201526287 |
Volume: | 51 |
Issue: | 4 |
Start Page Number: | 919 |
End Page Number: | 940 |
Publication Date: | Apr 2015 |
Journal: | Structural and Multidisciplinary Optimization |
Authors: | Amsallem David, Zahr Matthew, Choi Youngsoo, Farhat Charbel |
Keywords: | engineering, programming: nonlinear |
Solving large‐scale PDE‐constrained optimization problems presents computational challenges due to the large dimensional set of underlying equations that have to be handled by the optimizer. Recently, projection‐based nonlinear reduced‐order models have been proposed to be used in place of high‐dimensional models in a design optimization procedure. The dimensionality of the solution space is reduced using a reduced‐order basis constructed by Proper Orthogonal Decomposition. In the case of nonlinear equations, however, this is not sufficient to ensure that the cost associated with the optimization procedure does not scale with the high dimension. To achieve that goal, an additional reduction step,