The role of the state in model reduction with subspace and POD-based data-driven methods
Open access
Date
2021Type
- Conference Paper
Abstract
The paper investigates the selection of state sequences in data-driven projection-based model reduction methods that compute parsimonious models by forming regression problems featuring low-order fictitious states.
Specifically, subspace identification and dynamic mode decomposition techniques are considered. It is shown that, while sharing a seemingly equivalent structure, they differ profoundly in the way these states are selected. A theoretical characterization of the differences is given, including a parametrization of a new class of state transformations implicitly used in both approaches and a balanced transformation obtained directly from data. Numerical examples are proposed to show the impact of these differences on the accuracy of the extracted low-order representations. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000498452Publication status
publishedExternal links
Book title
2021 American Control Conference (ACC)Pages / Article No.
Publisher
IEEEEvent
Organisational unit
08814 - Smith, Roy (Tit.-Prof.)
Funding
178890 - Modeling, Identification and Control of Periodic Systems in Energy Applications (SNF)
Related publications and datasets
Is supplemented by: https://doi.org/10.3929/ethz-b-000498458
Notes
Conference lecture held on May 28, 2021More
Show all metadata