Evaluation of layer potentials close to the boundary for Laplace and Helmholtz problems on analytic planar domains (Q2874990)
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scientific article; zbMATH DE number 6329816
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Evaluation of layer potentials close to the boundary for Laplace and Helmholtz problems on analytic planar domains |
scientific article; zbMATH DE number 6329816 |
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13 August 2014
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layer potential
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Laplace equation
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Helmholtz equation
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convergence
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numerical example
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boundary integral equation
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Evaluation of layer potentials close to the boundary for Laplace and Helmholtz problems on analytic planar domains (English)
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This article considers boundary integral equations, since they are an efficient and accurate tool to solve numerically elliptic boundary value problems. Usually, the solution is expressed as a layer potential, but a problem arises if one uses a fixed quadrature rule. Precisely, the error of the layer potential evaluation grows large close to the boundary. First, the author analyzes this error for the Laplace equation with analytic density using a global periodic trapezoidal rule. He finds an intimate connection to the complexification of the boundary parametrization. Next, a simple and efficient scheme is given that evaluates accurately up to the boundary the single- and double layer potentials both for the Laplace and the Helmholtz equation. The author uses a surrogate expansion about centers that are placed near the boundary. It turns out that the scheme is asymptotically exponentially convergent. The author proves this for the analytic Laplace case. Additionally, the scheme does not require adaptivity, it easily generalizes to three dimensions, and it has \(\mathcal{O}(N)\) complexity when executed via a locally corrected fast multipole sum. Finally, an example of high-frequency scattering for an obstacle that has perimeter 700 wavelength long is presented. The evaluation of the solution at 20000 points close to the boundary with 11-digit accuracy is achieved in 30 seconds using MATLAB on a single CPU core.
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