Vector Fitting

TL;DR

The paper presents Vector Fitting, a data-driven algorithm for creating stable reduced-order models from sampled responses of linear systems, with applications in various modeling contexts.

Contribution

It introduces the Vector Fitting algorithm, including theory, implementation details, and extensions for time-domain, parametric, and distributed systems modeling.

Findings

Effective reduction of linear systems from experimental data

Stable reduced models can be constructed and adapted for different applications

Open-source implementation facilitates adoption and further development

Abstract

We introduce the Vector Fitting algorithm for the creation of reduced-order models from the sampled response of a linear time-invariant system. This data-driven approach to reduction is particularly useful when the system under modeling is known only through experimental measurements. The theory behind Vector Fitting is presented for single- and multiple-input systems, together with numerical details, pseudocodes, and an open-source implementation. We discuss how the reduced model can be made stable and converted to a variety of forms for use in virtually any modeling context. Finally, we survey recent extensions of the Vector Fitting algorithm geared towards time-domain, parametric and distributed systems modeling.