arXiv Analytics

Sign in

arXiv:2106.10224 [math.NA]AbstractReferencesReviewsResources

A new approach to proper orthogonal decomposition with difference quotients

Sarah K. Locke, John R. Singler

Published 2021-06-18Version 1

In a recent work [B. Koc et al., arXiv:2010.03750, SIAM J. Numer. Anal., to appear], the authors showed that including difference quotients (DQs) is necessary in order to prove optimal pointwise in time error bounds for proper orthogonal decomposition (POD) reduced order models of the heat equation. In this work, we introduce a new approach to including DQs in the POD procedure. Instead of computing the POD modes using all of the snapshot data and DQs, we only use the first snapshot along with all of the DQs and special POD weights. We show that this approach retains all of the numerical analysis benefits of the standard POD DQ approach, while using a POD data set that has half the number of snapshots as the standard POD DQ approach, i.e., the new approach is more computationally efficient. We illustrate our theoretical results with numerical experiments.

Related articles: Most relevant | Search more
arXiv:2305.04680 [math.NA] (Published 2023-05-08)
Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decomposition
arXiv:2109.12184 [math.NA] (Published 2021-09-24)
Reduced order modeling of nonlinear microstructures through Proper Orthogonal Decomposition
arXiv:1901.10719 [math.NA] (Published 2019-01-30)
Proper orthogonal decomposition (POD) combined with hierarchical tensor approximation (HTA) in the context of uncertain parameters