A New Model Dimension Reduction Technique Based on Finite Volume Element and Proper Orthogonal Decomposition for Solving the 2D Hyperbolic Equation

This article mainly researches the model dimension reduction in the finite volume element (FVE) method based on proper orthogonal decomposition (POD) for the two-dimensional (2D) hyperbolic equation. For this objective, an FVE method with unconditional stability and second-order temporal accuracy, and the existence, stability, and error estimates of the FVE solutions are first reviewed. Thereafter, most importantly, a new FVF model dimension reduction (FVEMDR) formulation is established by applying POD technology to lower the dimension of the vectors composed of unknown coefficients for the FVE solutions. The greatest contribution of this article is the theoretical analysis of the existence, unconditional stability, and error estimations for the FVEMDR solutions. Moreover, in computation, two sets of numerical simulations are provided to confirm the validity of the theoretical results and show the effectiveness of the FVEMRD formulation.

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Publication Details

Journal
Axioms
Published
2026-03-16
DOI
https://doi.org/10.3390/axioms15030223
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

A New Model Dimension Reduction Technique Based on Finite Volume Element and Proper Orthogonal Decomposition for Solving the 2D Hyperbolic Equation

Yuejie Li, Jie Yang, Zhendong Luo
Axioms
Model Reduction and Neural Networks
article

A New Model Dimension Reduction Technique Based on Finite Volume Element and Proper Orthogonal Decomposition for Solving the 2D Hyperbolic Equation

Yuejie Li, Jie Yang, Zhendong Luo
article en

Abstract

This article mainly researches the model dimension reduction in the finite volume element (FVE) method based on proper orthogonal decomposition (POD) for the two-dimensional (2D) hyperbolic equation. For this objective, an FVE method with unconditional stability and second-order temporal accuracy, and the existence, stability, and error estimates of the FVE solutions are first reviewed. Thereafter, most importantly, a new FVF model dimension reduction (FVEMDR) formulation is established by applying POD technology to lower the dimension of the vectors composed of unknown coefficients for the FVE solutions. The greatest contribution of this article is the theoretical analysis of the existence, unconditional stability, and error estimations for the FVEMDR solutions. Moreover, in computation, two sets of numerical simulations are provided to confirm the validity of the theoretical results and show the effectiveness of the FVEMRD formulation.

AxiomsVol. 15(3)
Nanchang University (CN), University Town of Shenzhen (CN), Shenzhen Technology University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 19%
Model Reduction and Neural Networks
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