PyDMD: A Python Package for Robust Dynamic Mode Decomposition
Authors: Sara M. Ichinaga, Francesco Andreuzzi, Nicola Demo, Marco Tezzele, Karl Lapo, Gianluigi Rozza, Steven L. Brunton, J. Nathan Kutz
JMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we introduce the version 1.0 release of Py DMD, which includes new data preprocessors, plotting tools, and a number of cutting-edge DMD methods specifically designed to handle real-world data that may be noisy, multiscale, parameterized, prohibitively high-dimensional, and even strongly nonlinear. ... Our code is unit tested, regularly maintained, and completely open-source under the MIT license. ... If we let X denote our data matrix, we can preprocess our data, perform DMD, and visualize the resulting spatiotemporal modes with the following code. ... Figure 1: Sample plot summary function output using fluid flow past a cylinder data with Reynolds number Re = 100. Data is available at dmdbook.com/DATA.zip. |
| Researcher Affiliation | Academia | 1 Department of Applied Mathematics, University of Washington, Seattle, WA 98195, USA 2 Mathematics Area, math Lab, SISSA, via Bonomea 265, I-34136 Trieste, Italy 3 Department of Mathematics, Emory University, Atlanta, GA 30322, USA 4 CERN, Geneva, Switzerland 5 Department of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria 6 Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA |
| Pseudocode | Yes | If we let X denote our data matrix, we can preprocess our data, perform DMD, and visualize the resulting spatiotemporal modes with the following code. ... 1 from pydmd import DMD 2 from pydmd.preprocessing import zero_mean_preprocessing 3 from pydmd.plotter import plot_summary 5 dmd = DMD(svd_rank =12) # Build DMD model. 6 dmd = zero_mean_preprocessing (dmd) # Wrap with data preprocessor. 7 dmd.fit(X) # Fit DMD model to snapshot data. 8 plot_summary(dmd) # Plot key spatiotemporal modes. |
| Open Source Code | Yes | The entire codebase is released under the MIT license and is available at https://github.com/Py DMD/Py DMD. |
| Open Datasets | Yes | Figure 1: Sample plot summary function output using fluid flow past a cylinder data with Reynolds number Re = 100. Data is available at dmdbook.com/DATA.zip. |
| Dataset Splits | No | No specific dataset split information (percentages, sample counts, or methodology for dividing data into training, validation, or test sets) is provided in the paper. The paper focuses on presenting a software package and its capabilities, demonstrating it with a sample fluid flow dataset without detailing experimental reproduction requirements for splits. |
| Hardware Specification | No | The paper describes a Python package and its features but does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for development, testing, or example execution. |
| Software Dependencies | No | The paper lists several standard Python libraries (Num Py, Sci Py, Matplotlib, Scikit-learn) but does not specify their version numbers, which are required for reproducible software dependencies. |
| Experiment Setup | Yes | 5 dmd = DMD(svd_rank =12) # Build DMD model. |