A greedy non-intrusive reduced order model for shallow water equations
Abstract
In this work, we develop Non-Intrusive Reduced Order Models (NIROMs) that combine Proper Orthogonal Decomposition (POD) with a Radial Basis Function (RBF) interpolation method to construct efficient reduced order models for time-dependent problems arising in large scale environmental flow applications. The performance of the POD-RBF NIROM is compared with a traditional nonlinear POD (NPOD) model by evaluating the accuracy and robustness for test problems representative of riverine flows. Different greedy algorithms are studied in order to determine a near-optimal distribution of interpolation points for the RBF approximation. A new power-scaled residual greedy (psr-greedy) algorithm is proposed to address some of the primary drawbacks of the existing greedy approaches. The relative performances of these greedy algorithms are studied with numerical experiments using realistic two-dimensional (2D) shallow water flow applications involving coastal and riverine dynamics.
Cite
@article{arxiv.2002.11329,
title = {A greedy non-intrusive reduced order model for shallow water equations},
author = {Sourav Dutta and Matthew W. Farthing and Emma Perracchione and Gaurav Savant and Mario Putti},
journal= {arXiv preprint arXiv:2002.11329},
year = {2025}
}