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Parametric dynamic mode decomposition for reduced order modeling
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DOI：10.1016/j.jcp.2022.111852.png)
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摘要
En 中文
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 multiple 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 eigen-pair 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.(c) 2022 Published by Elsevier Inc.
Keyword:
Dynamical mode decomposition
Parametric model dependence
Parametric model -order reduction
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IF:
3.8
论文数:
1.6W
被引数:
7.4W
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