Rational-approximation-based model order reduction of Helmholtz frequency response problems with adaptive finite element snapshots

TL;DR

This paper develops adaptive finite element-based model order reduction techniques for Helmholtz frequency response problems, improving approximation accuracy by tailoring snapshots to local singularities and frequency-specific features.

Contribution

It introduces spatially adaptive model order reduction methods using rational surrogates with adaptive finite element snapshots for Helmholtz problems, enhancing accuracy for resonant and scattering cases.

Findings

Adaptive methods effectively resolve local singularities.

V-SRI performs well for smooth exterior scattering problems.

Numerical experiments validate the proposed approaches.

Abstract

We introduce several spatially adaptive model order reduction approaches tailored to non-coercive elliptic boundary value problems, specifically, parametric-in-frequency Helmholtz problems. The offline information is computed by means of adaptive finite elements, so that each snapshot lives in a different discrete space that resolves the local singularities of the analytical solution and is adjusted to the considered frequency value. A rational surrogate is then assembled adopting either a least-squares or an interpolatory approach, yielding function-valued version of the standard rational interpolation method ($\mathcal{V}$-SRI) and the minimal rational interpolation method (MRI). In the context of building an approximation for linear or quadratic functionals of the Helmholtz solution, we perform several numerical experiments to compare the proposed methodologies. Our simulations show that, for interior resonant problems (whose singularities are encoded by poles on the real axis), the spatially adaptive $\mathcal{V}$-SRI and MRI work comparably well. Instead, when dealing with exterior scattering problems, whose frequency response is mostly smooth, the $\mathcal{V}$-SRI method seems to be the best-performing one.