Name
Title | ||
|---|---|---|
The role of the state in model reduction with subspace and POD-based data-driven methods |
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.23919/ACC50511.2021.9482920 | 2021 AMERICAN CONTROL CONFERENCE (ACC) |
DocType | ISSN | Citations |
Conference | 0743-1619 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Iannelli | 1 | 0 | 0.34 |
| Roy S. Smith | 2 | 0 | 0.34 |