This report introduces a novel signature-based method for system identification that is interpretable, expressive, and computationally simpler than common black-box models, demonstrating its potential for controlling nonlinear dynamical systems.

This work advances the HAVOK framework for analyzing chaotic dynamical systems by introducing a principled rank selection method, reducing data requirements, and leveraging singular vectors to predict rare events like lobe switching in the Lorenz attractor.