Time-limited pseudo-optimal H$_2$-model order reduction

TL;DR

This paper introduces a Krylov subspace-based algorithm for time-limited H2-model order reduction that produces high-fidelity reduced models within a specified time interval, suitable for large-scale systems.

Contribution

It presents a novel, efficient, iteration-free algorithm that enforces a subset of optimality conditions for time-limited H2 reduction and includes an adaptive framework for error decay.

Findings

Algorithm achieves high accuracy within the time interval.

Applicable to large-scale systems efficiently.

Validated on benchmark problems.

Abstract

A model order reduction algorithm is presented that generates a reduced-order model of the original high-order model, which ensures high-fidelity within the desired time interval. The reduced model satisfies a subset of the first-order optimality conditions for time-limited H$_2$-model reduction problem. The algorithm uses a computationally efficient Krylov subspace-based framework to generate the reduced model, and it is applicable to large-scale systems. The reduced-order model is parameterized to enforce a subset of the first-order optimality conditions in an iteration-free way. We also propose an adaptive framework of the algorithm, which ensures a monotonic decay in error irrespective of the choice of interpolation points and tangential directions. The efficacy of the algorithm is validated on benchmark model reduction problems.