Time-Domain Iterative Rational Krylov Method

TL;DR

This paper introduces a time-domain IRKA method that constructs $\

Contribution

It develops a time-domain variant of TF-IRKA that avoids repeated frequency data evaluations by leveraging a new eigenvalue formula and linear system scaling.

Findings

The proposed method constructs $\

The algorithm effectively produces $\

Numerical examples demonstrate the method's efficiency and accuracy.

Abstract

The Realization Independent Iterative Rational Krylov Algorithm (TF-IRKA) is a frequency-based data-driven reduced order modeling (DDROM) method that constructs $\mathcal H_2$ optimal DDROMs. However, as the $\mathcal H_2$ optimal approximation theory dictates, TF-IRKA requires repeated sampling of frequency data, that is, values of the system transfer function and its derivative, outside the unit circle. This repeated evaluation of frequency data requires repeated full model computations and may not be feasible. The data-informativity framework for moment matching provides a method for obtaining such frequency data from a single time-domain simulation. However, this framework usually requires solving linear systems with prohibitively ill-conditioned matrices, especially when recovering frequency data from off the unit circle as required for optimality. In this paper, building upon our previous work with the data informativity framework for moment matching, we provide a formula for the nonzero extreme eigenvalues of a symmetric rank-$1$ perturbation to an orthogonal projection, which then leads to an optimal scaling of the aforementioned linear systems. We also establish connections between the underlying dynamical system and conditioning of these linear systems. This analysis then leads to our algorithmic development, time-domain IRKA, which allows us to implement a time-domain variant of TF-IRKA, constructing $\mathcal H_2$ optimal DDROMs from a single time-domain simulation without requiring repeated frequency data evaluations. The numerical examples illustrate the effectiveness of the proposed algorithm.