Asymptotic-Preserving and Energy Stable Dynamical Low-Rank Approximation

IF 2.8 2区 数学 Q1 MATHEMATICS, APPLIED
Lukas Einkemmer, Jingwei Hu, Jonas Kusch
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引用次数: 0

Abstract

SIAM Journal on Numerical Analysis, Volume 62, Issue 1, Page 73-92, February 2024.
Abstract. Radiation transport problems are posed in a high-dimensional phase space, limiting the use of finely resolved numerical simulations. An emerging tool to efficiently reduce computational costs and memory footprint in such settings is dynamical low-rank approximation (DLRA). Despite its efficiency, numerical methods for DLRA need to be carefully constructed to guarantee stability while preserving crucial properties of the original problem. Important physical effects that one likes to preserve with DLRA include capturing the diffusion limit in the high-scattering regimes as well as dissipating energy. In this work we propose and analyze a dynamical low-rank method based on the “unconventional” basis update & Galerkin step integrator. We show that this method is asymptotic preserving, i.e., it captures the diffusion limit, and energy stable under a CFL condition. The derived CFL condition captures the transition from the hyperbolic to the parabolic regime when approaching the diffusion limit.
渐近保全和能量稳定的动态低方根近似法
SIAM 数值分析期刊》第 62 卷第 1 期第 73-92 页,2024 年 2 月。 摘要。辐射传输问题是在高维相空间中提出的,限制了精细解析数值模拟的使用。动态低阶近似(DLRA)是在这种情况下有效降低计算成本和内存占用的新兴工具。尽管 DLRA 效率很高,但它的数值方法需要精心构建,以保证稳定性,同时保留原始问题的关键属性。DLRA 需要保留的重要物理效应包括捕捉高散射情况下的扩散极限以及耗散能量。在这项工作中,我们提出并分析了一种基于 "非常规 "基础更新 & Galerkin 步积分器的动态低阶方法。我们的研究表明,这种方法具有渐近保留性,即它能捕捉到扩散极限,并且在 CFL 条件下能量稳定。导出的 CFL 条件捕捉到了在接近扩散极限时从双曲到抛物状态的过渡。
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来源期刊
CiteScore
4.80
自引率
6.90%
发文量
110
审稿时长
4-8 weeks
期刊介绍: SIAM Journal on Numerical Analysis (SINUM) contains research articles on the development and analysis of numerical methods. Topics include the rigorous study of convergence of algorithms, their accuracy, their stability, and their computational complexity. Also included are results in mathematical analysis that contribute to algorithm analysis, and computational results that demonstrate algorithm behavior and applicability.
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