Dynamics Modeling and Analysis with Data (DyMAD)¶
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 Roadmap for more details.
The code is hosted on GitHub.
Developers¶
The package is developed by the APUS Lab at Penn State, 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.