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

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. • Efficient POD-ROM combines DEIM, PID, and FV methods for hyperbolic systems. • Proves ROMs inherit well-balanced property from well-balanced FOMs. • DEIM strategy enhances efficiency, accuracy, and reduces spurious oscillations. • Predictive ROMs validated for parameterized systems via sensitivity analysis. • Applied to transport and Burgers equations with source terms and shallow water system with bottom and Manning friction.