Data-driven approximation and reduction from noisy data in matrix pencil frameworks
Systems and Control
2022-09-13 v2 Numerical Analysis
Systems and Control
Numerical Analysis
Abstract
This work aims at tackling the problem of learning surrogate models from noisy time-domain data by means of matrix pencil-based techniques, namely the Hankel and Loewner frameworks. A data-driven approach to obtain reduced-order state-space models from time-domain input-output measurements for linear time-invariant (LTI) systems is proposed. This is accomplished by combining the aforementioned model order reduction (MOR) techniques with the signal matrix model (SMM) approach. The proposed method is illustrated by a numerical benchmark example consisting of a building model.
Cite
@article{arxiv.2202.09568,
title = {Data-driven approximation and reduction from noisy data in matrix pencil frameworks},
author = {Pauline Kergus and Ion Victor Gosea},
journal= {arXiv preprint arXiv:2202.09568},
year = {2022}
}
Comments
10 pages, 10 figures