当前位置: X-MOL 学术Classical Quant. Grav. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
On the stability and accuracy of the empirical interpolation method and gravitational wave surrogates
Classical and Quantum Gravity ( IF 3.5 ) Pub Date : 2021-06-03 , DOI: 10.1088/1361-6382/abf894
Manuel Tiglio , Aarón Villanueva

The combination of the reduced basis and the empirical interpolation method (EIM) approaches have produced outstanding results in many disciplines. In particular, in gravitational wave (GW) science these results range from building non-intrusive surrogate models for GWs to fast parameter estimation adding the use of reduced order quadratures. These surrogates have the salient feature of being essentially indistinguishable from or very close to supercomputer simulations of the Einstein equations, but can be evaluated in the order of a millisecond per multipole mode on a standard laptop. In this article we clarify a common misperception of the EIM as originally introduced and used in practice in GW science. Namely, we prove that the EIM at each iteration chooses the interpolation nodes so as to make the related Vandermonde-type matrix as invertible as possible; not necessarily optimizing its conditioning or accuracy of the interpolant as is sometimes thought. In fact, we introduce two new variations of the EIM, nested as well, which do optimize with respect to conditioning and the Lebesgue constant, respectively, and compare them through numerical experiments with the original EIM using GWs. Our analyses and numerical results suggest a subtle relationship between solving for the original EIM, conditioning, and the Lebesgue constant, in consonance with active research in rigorous approximation theory and related fields.



中文翻译:

关于经验插值法和引力波替代品的稳定性和准确性

约简基和经验插值法 (EIM) 方法的结合在许多学科中产生了出色的结果。特别是,在引力波 (GW) 科学中,这些结果的范围从为 GW 构建非侵入式代理模型到快速参数估计以及使用降阶正交。这些替代品的显着特点是与爱因斯坦方程的超级计算机模拟基本上无法区分或非常接近,但可以在标准笔记本电脑上以每多极模式毫秒的数量级进行评估。在本文中,我们澄清了最初在 GW 科学中引入并在实践中使用的 EIM 的常见误解。即,我们证明了 EIM 在每次迭代时选择插值节点,以使相关的 Vandermonde 型矩阵尽可能可逆;不一定像有时认为的那样优化其条件或插值的准确性。事实上,我们引入了 EIM 的两个新变体,也是嵌套的,它们分别针对条件和勒贝格常数进行了优化,并通过数值实验将它们与使用 GW 的原始 EIM 进行了比较。我们的分析和数值结果表明,求解原始 EIM、条件和勒贝格常数之间存在微妙的关系,这与严格近似理论和相关领域的积极研究相一致。我们引入了 EIM 的两个新变体,也是嵌套的,它们分别针对条件和勒贝格常数进行了优化,并通过数值实验将它们与使用 GW 的原始 EIM 进行了比较。我们的分析和数值结果表明,求解原始 EIM、条件和勒贝格常数之间存在微妙的关系,这与严格近似理论和相关领域的积极研究相一致。我们引入了 EIM 的两个新变体,也是嵌套的,它们分别针对条件和勒贝格常数进行了优化,并通过数值实验将它们与使用 GW 的原始 EIM 进行了比较。我们的分析和数值结果表明,求解原始 EIM、条件和勒贝格常数之间存在微妙的关系,这与严格近似理论和相关领域的积极研究相一致。

更新日期:2021-06-03
down
wechat
bug