  Dynamic mode decomposition (DMD) is a versatile approach that enables the
construction of low-order models from data. Controller design tasks based on
such models require estimates and guarantees on predictive accuracy. In this
work, we provide a theoretical analysis of DMD model errors that reveals impact
of model order and data availability. The analysis also establishes conditions
under which DMD models can be made asymptotically exact. We verify our results
using a 2D diffusion system.
