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arXiv 1402.2018math.NAcs.NA
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非线性二维浅水方程的POD降阶策略比较

Comparison of POD reduced order strategies for the nonlinear 2D Shallow Water Equations

Virginia Polytechnic Institute and State University(弗吉尼亚理工大学);The Florida State University(佛罗里达州立大学)

Răzvan Ştefănescu, Adrian Sandu, Ionel M. Navon

AI总结:

本文比较了基于张量算术的POD降阶策略与标准POD在非线性项计算效率上的差异,发现张量POD在高次非线性项中表现更优,但实现成本更高。

摘要:

本文介绍了在正交分解(POD)框架下引入张量算术技术,以降低非线性项的计算复杂性。所提出的方法称为张量POD,可应用于任意次多项式非线性项。此类非线性项的在线复杂度为O(k^{p+1}),其中k是POD基底的维度,因此与全空间维度无关。然而,仅对二次非线性项有效,因为对于更高次非线性项,标准POD在增加POD基底维度k时证明效率更低。通过二维浅水方程(SWE)测试问题进行了数值实验,比较了张量POD、标准POD和POD/离散经验插值法(DEIM)的性能。数值结果表明,张量POD将标准POD在线阶段的计算成本降低了76倍,对于使用超过300,000个模型变量的配置。张量POD SWE模型仅比POD/DEIM SWE模型慢2-8倍,但实现成本显著增加。再次使用张量算术构造了新算法,使POD/DEIM浅水方程模型的离线阶段计算速度比标准和张量POD方法更快。

英文摘要:

This paper introduces tensorial calculus techniques in the framework of Proper Orthogonal Decomposition (POD) to reduce the computational complexity of the reduced nonlinear terms. The resulting method, named tensorial POD, can be applied to polynomial nonlinearities of any degree $p$. Such nonlinear terms have an on-line complexity of $\mathcal{O}(k^{p+1})$, where $k$ is the dimension of POD basis, and therefore is independent of full space dimension. However it is efficient only for quadratic nonlinear terms since for higher nonlinearities standard POD proves to be less time consuming once the POD basis dimension $k$ is increased. Numerical experiments are carried out with a two dimensional shallow water equation (SWE) test problem to compare the performance of tensorial POD, standard POD, and POD/Discrete Empirical Interpolation Method (DEIM). Numerical results show that tensorial POD decreases by $76\times$ times the computational cost of the on-line stage of standard POD for configurations using more than $300,000$ model variables. The tensorial POD SWE model was only $2-8\times$ slower than the POD/DEIM SWE model but the implementation effort is considerably increased. Tensorial calculus was again employed to construct a new algorithm allowing POD/DEIM shallow water equation model to compute its off-line stage faster than the standard and tensorial POD approaches.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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