Full title - The Dynamic Mode Decomposition - A Data-Driven Algorithm for the Analysis of Complex Systems
The dynamic mode decomposition (DMD) is a powerful data-driven modeling technique that reveals coherent spatiotemporal structures and produces reconstructions and future-state predictions from data. The method's simple linear algebra-based formulation additionally allows for a variety of optimizations and extensions that make the algorithm more practical and viable for analyzing real-world data sets. As a result, DMD has grown to become a leading method for equation-free system analysis across multiple scientific disciplines. PyDMD is a Python package that implements DMD and several of its major variants. In this talk, I will go over the underlying DMD theory and show the use of the PyDMD package which is specifically designed to handle dynamics that are noisy, multiscale, parameterized, prohibitively high-dimensional, or even strongly nonlinear. I will additionally provide a complete overview of the features currently available in PyDMD, along with a brief overview of the theory behind the DMD algorithm, tips regarding practical DMD usage, information for developers, and coding examples.