S-OPT: A Points Selection Algorithm for Hyper-Reduction in Reduced Order Models
Abstract
While projection-based reduced order models can reduce the dimension of full order solutions, the resulting reduced models may still contain terms that scale with the full order dimension. Hyper-reduction techniques are sampling-based methods that further reduce this computational complexity by approximating such terms with a much smaller dimension. The goal of this work is to introduce a points selection algorithm developed by Shin and Xiu [SIAM J. Sci. Comput., 38 (2016), pp. A385--A411], as a hyper-reduction method. The selection algorithm is originally proposed as a stochastic collocation method for uncertainty quantification. Since the algorithm aims at maximizing a quantity S that measures both the column orthogonality and the determinant, we refer to the algorithm as S-OPT. Numerical examples are provided to demonstrate the performance of S-OPT and to compare its performance with an over-sampled Discrete Empirical Interpolation (DEIM) algorithm. We found that using the S-OPT algorithm is shown to predict the full order solutions with higher accuracy for a given number of indices.
Cite
@article{arxiv.2203.16494,
title = {S-OPT: A Points Selection Algorithm for Hyper-Reduction in Reduced Order Models},
author = {Jessica T. Lauzon and Siu Wun Cheung and Yeonjong Shin and Youngsoo Choi and Dylan Matthew Copeland and Kevin Huynh},
journal= {arXiv preprint arXiv:2203.16494},
year = {2024}
}
Comments
26 pages, 15 figures, submitted to SIAM Journal of Scientific Computing