Dynamics Modeling and Analysis with Data (DyMAD)
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DyMAD aims to provide a lightweight and user-friendly toolkit for modeling and analyzing dynamical systems using data-driven approaches, plus an agentic interface for users to interact with the package and build their own workflows.

Currently, we have implemented the following features:

- Data preprocessing pipeline specialized for time series data.
- Models for different types of dynamical systems, including
   * Latent Dynamics Models
   * Koopman Bilinear Forms
   * The graph version of the above
- Training utilities, including
   * Neural-ODE-based optimizer
   * Weak-form optimizer
   * Linear preconditioners
   * Hyperparameter tuning tools
- Spectral analysis based on Koopman theory
   * Eigenvalues and eigenfunctions
   * Spectrum and pseudospectrum
- Miscellaneous utilities, including
   * Samplers for inputs and initial conditions
   * Visualization tools

Plus the agentic interface for all the above features.

It is still far from complete, see our :doc:`Roadmap <roadmap>` for more details.

The code is hosted on `GitHub <https://github.com/apus-lab/dymad>`_.


Developers
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The package is developed by the `APUS Lab at Penn State <https://apus-lab.github.io/>`_, directed by Dr. Daning Huang.

The initial development was led by Dr. Yin Yu, whose PhD thesis is on the topic of data-driven modeling of dynamical systems on graphs.


Explore More
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.. toctree::
   :maxdepth: 1

   getting_started
   agent_cli_workflow
   roadmap
   examples
   theory
   api_ref
   developer