Efficient approximation of high-frequency Helmholtz solutions by Gaussian coherent states

IF 2.1 2区 数学 Q1 MATHEMATICS, APPLIED Numerische Mathematik Pub Date : 2024-07-04 DOI:10.1007/s00211-024-01411-0
T. Chaumont-Frelet, V. Dolean, M. Ingremeau
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Abstract

We introduce new finite-dimensional spaces specifically designed to approximate the solutions to high-frequency Helmholtz problems with smooth variable coefficients in dimension d. These discretization spaces are spanned by Gaussian coherent states, that have the key property to be localised in phase space. We carefully select the Gaussian coherent states spanning the approximation space by exploiting the (known) micro-localisation properties of the solution. For a large class of source terms (including plane-wave scattering problems), this choice leads to discrete spaces that provide a uniform approximation error for all wavenumber k with a number of degrees of freedom scaling as \(k^{d-1/2}\), which we rigorously establish. In comparison, for discretization spaces based on (piecewise) polynomials, the number of degrees of freedom has to scale at least as \(k^d\) to achieve the same property. These theoretical results are illustrated by one-dimensional numerical examples, where the proposed discretization spaces are coupled with a least-squares variational formulation.

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用高斯相干态高效逼近高频亥姆霍兹解法
我们引入了新的有限维空间,专门用于近似 d 维平滑可变系数的高频亥姆霍兹问题的解。这些离散空间由高斯相干态跨越,高斯相干态具有在相空间中定位的关键特性。我们利用求解的(已知)微定位特性,精心选择了跨越近似空间的高斯相干态。对于一大类源项(包括平面波散射问题),这种选择会导致离散空间,为所有波长 k 提供均匀的近似误差,其自由度数缩放为 \(k^{d-1/2}\),我们严格地确定了这一点。相比之下,对于基于(片断)多项式的离散空间,自由度的数量至少要达到 \(k^{d\) 才能实现相同的特性。这些理论结果通过一维数值示例进行了说明,在这些示例中,所提出的离散化空间与最小二乘变分公式相结合。
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来源期刊
Numerische Mathematik
Numerische Mathematik 数学-应用数学
CiteScore
4.10
自引率
4.80%
发文量
72
审稿时长
6-12 weeks
期刊介绍: Numerische Mathematik publishes papers of the very highest quality presenting significantly new and important developments in all areas of Numerical Analysis. "Numerical Analysis" is here understood in its most general sense, as that part of Mathematics that covers: 1. The conception and mathematical analysis of efficient numerical schemes actually used on computers (the "core" of Numerical Analysis) 2. Optimization and Control Theory 3. Mathematical Modeling 4. The mathematical aspects of Scientific Computing
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