A Reduced Weak Galerkin Finite Element Formulation Based on the POD Method for a Semilinear Parabolic Equation

Abstract In this paper, we study a reduced-order model based on proper orthogonal decomposition (POD) for a two-dimensional semilinear parabolic equation. The weak Galerkin finite element method (WG-FEM) is used for spatial discretization and the Crank–Nicolson (CN) scheme is employed for temporal discretization. We introduce the POD formulation and establish the optimal error estimates for both WG-FEM and WG-POD solutions, achieving second-order accuracy in time and <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mi>O</m:mi> <m:mo>⁢</m:mo> <m:mrow> <m:mo stretchy="false">(</m:mo> <m:msup> <m:mi>h</m:mi> <m:mrow> <m:mi>k</m:mi> <m:mo>+</m:mo> <m:mn>1</m:mn> </m:mrow> </m:msup> <m:mo stretchy="false">)</m:mo> </m:mrow> </m:mrow> </m:math> O(h^{k+1}) accuracy in space in the <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msup> <m:mi>L</m:mi> <m:mn>2</m:mn> </m:msup> </m:math> L^{2} norm, and the numerical results are consistent with the theoretical conclusions. This indicates that the method can significantly reduce the degrees of freedom and save CPU time while maintaining high accuracy.

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Publication Details

Journal
Computational Methods in Applied Mathematics
Published
2026-06-25
DOI
https://doi.org/10.1515/cmam-2025-0194
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

A Reduced Weak Galerkin Finite Element Formulation Based on the POD Method for a Semilinear Parabolic Equation

Jintao Cui, Fuzheng Gao, Zihan Zheng
Computational Methods in Applied Mathematics
Model Reduction and Neural Networks
article

A Reduced Weak Galerkin Finite Element Formulation Based on the POD Method for a Semilinear Parabolic Equation

Jintao Cui, Fuzheng Gao, Zihan Zheng
article en

Abstract

Abstract In this paper, we study a reduced-order model based on proper orthogonal decomposition (POD) for a two-dimensional semilinear parabolic equation. The weak Galerkin finite element method (WG-FEM) is used for spatial discretization and the Crank–Nicolson (CN) scheme is employed for temporal discretization. We introduce the POD formulation and establish the optimal error estimates for both WG-FEM and WG-POD solutions, achieving second-order accuracy in time and <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mi>O</m:mi> <m:mo>⁢</m:mo> <m:mrow> <m:mo stretchy="false">(</m:mo> <m:msup> <m:mi>h</m:mi> <m:mrow> <m:mi>k</m:mi> <m:mo>+</m:mo> <m:mn>1</m:mn> </m:mrow> </m:msup> <m:mo stretchy="false">)</m:mo> </m:mrow> </m:mrow> </m:math> O(h^{k+1}) accuracy in space in the <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msup> <m:mi>L</m:mi> <m:mn>2</m:mn> </m:msup> </m:math> L^{2} norm, and the numerical results are consistent with the theoretical conclusions. This indicates that the method can significantly reduce the degrees of freedom and save CPU time while maintaining high accuracy.

Computational Methods in Applied Mathematics
Shandong University (CN), Jinan University (CN)
National Natural Science Foundation of China, Basic and Applied Basic Research Foundation of Guangdong Province
Peace, Justice and strong institutions
Openalex Percentile: Top 6%
Model Reduction and Neural Networks
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