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Communication Dans Un Congrès Année : 2022

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

Résumé

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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Dates et versions

hal-03590972 , version 1 (13-10-2023)

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Pauline Kergus, Ion Victor Gosea. Data-driven approximation and reduction from noisy data in matrix pencil frameworks. 25th IFAC Symposium on Mathematical Theory of Networks and Systems MTNS 2022, Sep 2022, Bayreuth, Germany. ⟨10.1016/j.ifacol.2022.11.081⟩. ⟨hal-03590972⟩
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