A Derivative-Orthogonal Wavelet Multiscale Method for 1D Elliptic Equations with Rough Diffusion Coefficients
Qiwei Feng, Bin Han

TL;DR
This paper introduces a derivative-orthogonal wavelet multiscale method for 1D elliptic equations with rough, high-contrast coefficients, achieving robust, accurate solutions on coarse meshes with proven error bounds and stable condition numbers.
Contribution
The paper develops a novel wavelet-based framework that ensures condition number bounds independent of mesh size and provides first- and second-order error convergence for rough coefficients.
Findings
Condition number bounded by the ratio of max to min coefficient values
First-order energy norm and second-order L2 norm error convergence
Effective handling of high-frequency, high-contrast coefficient oscillations
Abstract
In this paper, we investigate 1D elliptic equations with rough diffusion coefficients that satisfy and . To achieve an accurate and robust numerical solution on a coarse mesh of size , we introduce a derivative-orthogonal wavelet-based framework. This approach incorporates both regular and specialized basis functions constructed through a novel technique, defining a basis function space that enables effective approximation. We develop a derivative-orthogonal wavelet multiscale method tailored for this framework, proving that the condition number of the stiffness matrix satisfies , independent of . For the error analysis, we establish that the energy and -norm errors of our method converge at first-order and second-order rates, respectively, for any…
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Taxonomy
TopicsAdvanced Mathematical Modeling in Engineering · Image and Signal Denoising Methods · Numerical methods in inverse problems
