Well-balanced POD-based reduced-order models for finite volume approximation of hyperbolic balance laws

TL;DR

This paper presents a novel POD-based reduced-order modeling approach for hyperbolic systems using finite volume methods, enhancing efficiency and accuracy while maintaining well-balanced properties, validated through numerical experiments.

Contribution

It introduces a new reduced-order model combining POD, DEIM, and PID for hyperbolic systems, with theoretical justification and robustness analysis.

Findings

Significant computational efficiency improvements.

High accuracy in numerical simulations.

Robustness confirmed through sensitivity analysis.

Abstract

This paper introduces a reduced-order modeling approach based on finite volume methods for hyperbolic systems, combining Proper Orthogonal Decomposition (POD) with the Discrete Empirical Interpolation Method (DEIM) and Proper Interval Decomposition (PID). Applied to systems such as the transport equation with source term, non-homogeneous Burgers equation, and shallow water equations with non-flat bathymetry and Manning friction, this method achieves significant improvements in computational efficiency and accuracy compared to previous time-averaging techniques. A theoretical result justifying the use of well-balanced Full-Order Models (FOMs) is presented. Numerical experiments validate the approach, demonstrating its accuracy and efficiency. Furthermore, the question of prediction of solutions for systems that depend on some physical parameters is also addressed, and a sensitivity analysis on POD parameters confirms the model's robustness and efficiency in this case.