On the stability and accuracy of the Empirical Interpolation Method and Gravitational Wave Surrogates

TL;DR

This paper analyzes the Empirical Interpolation Method's stability and accuracy in gravitational wave surrogate modeling, clarifies its node selection process, and introduces two optimized variants to improve conditioning and accuracy.

Contribution

It clarifies the node selection mechanism of the EIM in GW science and introduces two new EIM variants optimized for conditioning and Lebesgue constant.

Findings

Original EIM chooses nodes to maximize matrix invertibility

Two new EIM variants improve conditioning and Lebesgue constant

Numerical experiments compare EIM variants in GW surrogate models

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 milisecond 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.