A low-rank algorithm for strongly damped wave equations with visco-elastic damping and mass terms

Damped wave equations have been used in many real-world simulations. In this paper, we study a low-rank solution method of the strongly damped wave equation with the damping term, visco-elastic damping term and mass term. Firstly, a second-order finite difference method is employed for spatial discretization. Then, we receive a system of second-order matrix differential equations. Next, we transform it into an equivalent system of first-order matrix differential equations, and split the transformed system into three subproblems. Applying a Strang splitting to such subproblems and combining a dynamical low-rank approach, we obtain a low-rank algorithm. Numerical experiments are reported to demonstrate that the proposed low-rank algorithm is robust and accurate, and has the second-order convergence rate in time.

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Publication Details

Journal
ESAIM. Mathematical modelling and numerical analysis
Published
2025-05-01
DOI
https://doi.org/10.1051/m2an/2025042
Citations
3
Primary Topic
Numerical methods in inverse problems
Type
article
Field-Weighted Citation Impact
11.08

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article

A low-rank algorithm for strongly damped wave equations with visco-elastic damping and mass terms

Xian‐Ming Gu, Yongliang Zhao
3 citations
ESAIM. Mathematical modelling and numerical analysis
Numerical methods in inverse problems
11.08
article

A low-rank algorithm for strongly damped wave equations with visco-elastic damping and mass terms

Xian‐Ming Gu, Yongliang Zhao
article en
3 citations

Abstract

Damped wave equations have been used in many real-world simulations. In this paper, we study a low-rank solution method of the strongly damped wave equation with the damping term, visco-elastic damping term and mass term. Firstly, a second-order finite difference method is employed for spatial discretization. Then, we receive a system of second-order matrix differential equations. Next, we transform it into an equivalent system of first-order matrix differential equations, and split the transformed system into three subproblems. Applying a Strang splitting to such subproblems and combining a dynamical low-rank approach, we obtain a low-rank algorithm. Numerical experiments are reported to demonstrate that the proposed low-rank algorithm is robust and accurate, and has the second-order convergence rate in time.

ESAIM. Mathematical modelling and numerical analysisVol. 59(3)
Southwestern University of Finance and Economics (CN), Sichuan Normal University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 2%
Numerical methods in inverse problems
11.08
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