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.

1. [Recommended] Create a Python environment, e.g. Conda environment:

conda create -n dmd pip numpy scipy
conda activate dmd

2. Clone the GitHub repository:

git clone git@github.com:imaliyov/dmd.git

3. Navigate to the dmd folder and install the package:

cd dmd
pip install .

Navigate to the examples folder and run one of the example files:

cd examples
./example<...>.py

1. 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

2. 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

3. 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

4. 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