A Python implementation of the standard and high-order dynamic mode decomposition, DMD[1-3] and HODMD[4]. The most time consuming part, the singular value decomposition (SVD) is performed using numpy. Loops over the space are vectorized.
- [Recommended] Create a Python environment, e.g. Conda environment:
conda create -n dmd pip numpy scipy
conda activate dmd- Clone the GitHub repository:
git clone [email protected]:imaliyov/dmd.git- Navigate to the
dmdfolder and install the package:
cd dmd
pip install .Navigate to the examples folder and run one of the example files:
cd examples
./example<...>.py-
S. L Brunton and J. N. Kutz, Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control, Cambridge University Press, 2022. DOI: 10.1017/9781108380690
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J. Yin, Y. Chan, F. H. da Jornada, D. Y. Qiu, C. Yang, and S. G. Louie, Analyzing and predicting non-equilibrium many-body dynamics via dynamic mode decomposition, J. Comput. Phys., 477, 2023, pp. 111909. DOI: 10.1016/j.jcp.2023.111909
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I. Maliyov, J. Yin, J. Yao, C. Yang, and M. Bernardi, Dynamic mode decomposition of nonequilibrium electron-phonon dynamics: accelerating the first-principles real-time Boltzmann equation, arXiv: 2311.07520
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S. Le Clainche and J. M. Vega, Higher Order Dynamic Mode Decomposition, SIAM Journal on Applied Dynamical Systems, 16, no. 2, pp. 882–925, 2017. DOI: 10.1137/15M1054924