Parametric dynamic mode decomposition for reduced order modeling

Huhn, Quincy A.; Tano, Mauricio E.; Ragusa, Jean C.; Choi, Youngsoo

doi:10.1016/j.jcp.2022.111852

Parametric dynamic mode decomposition for reduced order modeling

Journal Article · Mon Dec 12 00:00:00 EST 2022 · Journal of Computational Physics

DOI:https://doi.org/10.1016/j.jcp.2022.111852· OSTI ID:1970208

^[1]; Tano, Mauricio E. ^[1]; ^[1]; Choi, Youngsoo ^[2]

Texas A & M University, College Station, TX (United States)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

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. Here 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.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1970208

Alternate ID(s):: OSTI ID: 1961008

Report Number(s):: LLNL-JRNL-834453; 1053101

Journal Information:: Journal of Computational Physics, Journal Name: Journal of Computational Physics Journal Issue: N/A Vol. 475; ISSN 0021-9991

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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