Data-driven approximation and reduction from noisy data in matrix pencil frameworks

Pauline Kergus; Ion Victor Gosea

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arXiv · 2202.09568

Data-driven approximation and reduction from noisy data in matrix pencil frameworks

Pauline Kergus·Ion Victor Gosea

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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.

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BibTeX RIS

Pauline Kergus, Ion Victor Gosea. 2022-02-19. Data-driven approximation and reduction from noisy data in matrix pencil frameworks. https://arxiv.org/abs/2202.09568

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