2022/02/19 by Pauline Kergus, Kergus, Pauline, Ion Victor Gosea +1
Engineering · Materials Science · Physics and Astronomy · #93A15 #93C05 #93C73 #93C80 #Electromagnetic Simulation and Numerical Methods #FOS: Electrical engineering #FOS: Mathematics #Magnetic Properties and Applications #Model Reduction and Neural Networks #Numerical Analysis (math.NA) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2202.09568
openalex publication_date 2022/02/19 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
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.