2020/09/30 by Manuel Tiglio, Aarón Villanueva · 3 citations
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Applied mathematics #Classical mechanics #Computer science #Fractional Differential Equations Solutions #Gravitational wave #Interpolation (computer graphics) #Lagrange polynomial #Mathematical analysis #Mathematics #Model Reduction and Neural Networks #Multipole expansion #Numerical methods for differential equations #Physics #Polynomial #Quantum mechanics #Stability (learning theory) #cs.NA #gr-qc #math.NA
paper · pdf · doi:10.1088/1361-6382/abf894
published in Classical and Quantum Gravity 38(13), 135011 (IOP Publishing) · Accepted in Classical and Quantum Gravity. 16 pages, 7 figures. In this version the section on Fekete nodes was left for future research
openalex created_date 2020/09/21 · openalex publication_date 2021/04/15 · arxiv created 2021/04/17 · arxiv updated 2021/07/07 · openalex updated_date 2026/08/05
Abstract 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.