  Models for the evolution of the solar coronal magnetic field are vital for
understanding solar activity, yet the best measurements of magnetic field lie
at the photosphere, necessitating the development of coronal models which are
\textit{"data-driven"} at the photosphere. We present an investigation to
determine the feasibility and accuracy of such methods. Our validation
framework uses a simulation of active region (AR) formation, modeling the
emergence of magnetic flux from the convection zone to the corona, as a
ground-truth dataset, to supply both the photospheric information, and to
perform the validation of the data-driven method. We focus our investigation on
how the data-driven model accuracy depends on the temporal frequency of the
driving data. The Helioseismic and Magnetic Imager on NASA's Solar {\jl
Dynamics} Observatory produces full-disk vector magnetic field measurements at
a 12 minute cadence. Using our framework we show that ARs that emerge over 25
hours can be modeled by the data-driving method with only $\sim$1\% error in
the free magnetic energy, assuming the photospheric information is specified
every 12 minutes. However, for rapidly evolving features, under-sampling of the
dynamics at this cadence leads to a strobe effect, generating large electric
currents and incorrect coronal morphology and energies. We derive a sampling
condition for the driving cadence based on the evolution of these small-scale
features, and show that higher-cadence driving can lead to acceptable errors.
Future work will investigate the source of errors associated with deriving
plasma variables from the photospheric magnetograms as well as other sources of
errors such as reduced resolution and instrument bias and noise.
