A New Proper Orthogonal Decomposition Method with Second Difference Quotients for the Wave Equation

TL;DR

This paper introduces a novel proper orthogonal decomposition (POD) method using second difference quotients (DDQs) that reduces data redundancy and maintains error bounds, improving efficiency in wave equation simulations.

Contribution

It extends the DDQ POD approach to use only one snapshot and one DQ, providing efficient error bounds and reducing data redundancy in reduced order models.

Findings

The new DDQ POD method achieves comparable pointwise error bounds to standard methods.

Numerical results confirm the theoretical error bounds and efficiency improvements.

Application to wave equation ROMs demonstrates practical benefits of the approach.

Abstract

Recently, researchers have investigated the relationship between proper orthogonal decomposition (POD), difference quotients (DQs), and pointwise in time error bounds for POD reduced order models of partial differential equations. In a recent work (Eskew and Singler, Adv. Comput. Math., 49, 2023, no. 2, Paper No. 13), a new approach to POD with DQs was developed that is more computationally efficient than the standard DQ POD approach and it also retains the guaranteed pointwise in time error bounds of the standard method. In this work, we extend this new DQ POD approach to the case of second difference quotients (DDQs). Specifically, a new POD method utilizing DDQs and only one snapshot and one DQ is developed and used to prove ROM error bounds for the damped wave equation. This new approach eliminates data redundancy in the standard DDQ POD approach that uses all of the snapshots, DQs, and DDQs. We show that this new DDQ approach also has pointwise in time data error bounds similar to DQ POD and use it to prove pointwise and energy ROM error bounds. We provide numerical results for the POD errors and ROM errors to demonstrate the theoretical results. We also explore an application of POD to simulating ROMs past the training interval for collecting the snapshot data for the standard POD approach and the DDQ POD method.