
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.
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Taxonomy
TopicsModel Reduction and Neural Networks · Control Systems and Identification · Bladed Disk Vibration Dynamics
