Numerical Integration of the Function with Twin Boundary Layers

Traditional numerical integration requires sufficient smoothness of the integrand to achieve high-order algebraic accuracy. If the function has a boundary layer with large gradient, the composite integration formula on the uniform mesh will produce very large integration errors. In this paper, we study the Newton-Cotes formula based on Lagrange interpolation functions, local [see formula in PDF] projection approximating the integrand, and Gauss integration. On the Shishkin mesh, we establish an optimal-order integration error estimate uniformly in the perturbation parameter. The convergence rate is the same as that for the smooth function. Numerical experiments confirm the sharpness of our theoretical results.

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Publication Details

Journal
Wuhan University Journal of Natural Sciences
Published
2025-04-01
DOI
https://doi.org/10.1051/wujns/2025302139
Primary Topic
Differential Equations and Numerical Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Numerical Integration of the Function with Twin Boundary Layers

Yao Cheng, Juan KANG
Wuhan University Journal of Natural Sciences
Differential Equations and Numerical Methods
article

Numerical Integration of the Function with Twin Boundary Layers

Yao Cheng, Juan KANG
article en

Abstract

Traditional numerical integration requires sufficient smoothness of the integrand to achieve high-order algebraic accuracy. If the function has a boundary layer with large gradient, the composite integration formula on the uniform mesh will produce very large integration errors. In this paper, we study the Newton-Cotes formula based on Lagrange interpolation functions, local [see formula in PDF] projection approximating the integrand, and Gauss integration. On the Shishkin mesh, we establish an optimal-order integration error estimate uniformly in the perturbation parameter. The convergence rate is the same as that for the smooth function. Numerical experiments confirm the sharpness of our theoretical results.

Wuhan University Journal of Natural SciencesVol. 30(2)
Suzhou University of Science and Technology (CN)
National Natural Science Foundation of China, Jiangsu University, Government of Jiangsu Province, Natural Science Foundation of Jiangsu Province, Qinglan Project of Jiangsu Province of China
Openalex Percentile: Top 84%
Differential Equations and Numerical Methods
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