Samenvatting
Traditional model reduction derives reduced models from large-scale systems in a one-time computationally expensive offline (training) phase and then evaluates reduced models in an online phase to rapidly predict system outputs; however, this offline/online splitting means that reduced models can be expected to faithfully predict outputs only for system behavior that has been incorporated into the reduced models during the offline phase. This work considers model reduction with the online adaptive empirical interpolation method (AADEIM) that adapts reduced models in the online phase to system behavior that was not anticipated in the offline phase by deriving updates from a few samples of the states of the large-scale systems. The contribution of this work is an analysis of the AADEIM sampling strategy for deciding which parts of the large-scale states to sample to learn reduced-model updates. The analysis shows that the AADEIM sampling strategy is optimal up to a factor 2. Numerical results demonstrate the theoretical results by comparing the quasi-optimal AADEIM sampling strategy to other sampling strategies on various examples.
| Originele taal-2 | Engels |
|---|---|
| Titel | 2020 American Control Conference, ACC 2020 |
| Uitgeverij | Institute of Electrical and Electronics Engineers |
| Pagina's | 2472-2477 |
| Aantal pagina's | 6 |
| ISBN van elektronische versie | 9781538682661 |
| DOI's | |
| Status | Gepubliceerd - jul. 2020 |
| Evenement | 2020 American Control Conference, ACC 2020 - Denver, Verenigde Staten van Amerika Duur: 1 jul. 2020 → 3 jul. 2020 http://acc2020.a2c2.org/ |
Congres
| Congres | 2020 American Control Conference, ACC 2020 |
|---|---|
| Verkorte titel | ACC 2020 |
| Land/Regio | Verenigde Staten van Amerika |
| Stad | Denver |
| Periode | 1/07/20 → 3/07/20 |
| Internet adres |
Bibliografische nota
Publisher Copyright:© 2020 AACC.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
Financiering
2Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA. [email protected] *The work of the first and third author has been supported by the SNSF research project Fast algorithms from low-rank updates, grant number: 200020 178806. The fourth author was partially supported by the Air Force Center of Excellence on Multi-Fidelity Modeling of Rocket Combustor Dynamics, Award Number FA9550-17-1-0195 and the AFOSR MURI on multi-information sources of multi-physics systems under Award Number FA9550-15-1-0038. The numerical experiments were computed with support through the NYU IT High Performance Computing resources, services, and staff expertise.