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, robustness, and speed 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 that overcomes the drawbacks of the existing greedy approaches to enhance the accuracy and efficiency of the RBF approximation. The relative performance of these greedy algorithms is studied with numerical experiments using realistic 2D shallow water flow applications involving coastal and riverine dynamics.