Parametric Dynamic Mode Decomposition for Reduced Order Modeling
| Autor: | Quincy A. Huhn, Mauricio E. Tano, Jean C. Ragusa, Youngsoo Choi |
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| Rok vydání: | 2022 |
| Předmět: |
Computational Mathematics
Numerical Analysis Physics and Astronomy (miscellaneous) Applied Mathematics Modeling and Simulation FOS: Mathematics FOS: Physical sciences Mathematics - Numerical Analysis Numerical Analysis (math.NA) Computational Physics (physics.comp-ph) Physics - Computational Physics Computer Science Applications |
| DOI: | 10.48550/arxiv.2204.12006 |
| Popis: | Dynamic Mode Decomposition (DMD) is a model-order reduction approach, whereby spatial modes of fixed temporal frequencies are extracted from numerical or experimental data sets. The DMD low-rank or reduced operator is typically obtained by singular value decomposition of the temporal data sets. For parameter-dependent models, as found in many multi-query applications such as uncertainty quantification or design optimization, the only parametric DMD technique developed was a stacked approach, with data sets at multiples parameter values were aggregated together, increasing the computational work needed to devise low-rank dynamical reduced-order models. In this paper, we present two novel approach to carry out parametric DMD: one based on the interpolation of the reduced-order DMD eigenpair and the other based on the interpolation of the reduced DMD (Koopman) operator. Numerical results are presented for diffusion-dominated nonlinear dynamical problems, including a multiphysics radiative transfer example. All three parametric DMD approaches are compared. Comment: 29 pages, 10 figures |
| Databáze: | OpenAIRE |
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