PyDMD/PyDMD

Health v2 · maintenance only

Adoption not part of the score

1251 stars · 357 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PyDMD is a Python package implementing Dynamic Mode Decomposition (DMD) and its many variants (mrDMD, HODMD, etc.) for extracting spatiotemporal coherent structures from time-varying data. It integrates with NumPy/SciPy and offers visualization tools for mode reconstruction, energy spectra, and time evolution.

Use cases

• decompose time-varying simulation data into spatial and temporal modes
• perform model order reduction on high-dimensional dynamical systems
• analyze fluid dynamics flows for coherent structures
• extract dominant modes from noisy or large datasets
• apply DMD with control variables or imposed physical structure
• reconstruct and visualize the temporal evolution of system dynamics

When to choose

• you need data-driven decomposition of spatiotemporal datasets in Python
• you want access to many DMD variants (mrDMD, HODMD, forward-backward, etc.) in one package
• you work in the scientific Python ecosystem with NumPy/SciPy
• you need a well-documented, actively maintained, NumPy-affiliated research library

When to avoid

• you need real-time streaming decomposition rather than offline analysis
• your problem is standard time-series forecasting better served by ARIMA or deep learning models
• you require GPU-accelerated or distributed computation out of the box
• you need a GUI-based analysis tool rather than a Python API

Facets

library · maturity active

data-science machine-learning math simulation data-science machine-learning python cross-platform dynamic-mode-decomposition dmd model-order-reduction scientific-machine-learning numfocus time-series-analysis fluid-dynamics algorithms scientific-computing

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