  Understanding large-angular-scale galactic foregrounds is crucial for future
CMB experiments aiming to detect $B$-mode polarization from primordial
gravitational waves. Traditionally, the dust component has been separated using
its different frequency dependence. However, using non-CMB observations has
potential to increase fidelity and decrease the reconstruction noise. In this
exploratory paper we investigate the capability of galactic 21 cm observations
to predict the dust foreground in intensity. We train a neural network to
predict the dust foreground as measured by the Planck Satellite from the full
velocity data-cube of galactic 21 cm emission as measured by the HI4PI survey.
We demonstrate that information in the velocity structure clearly improves the
predictive power over both a simple integrated emission model and a simple
linear model. The improvement is significant at arc-minute scales but more
modest at degree scales. This proof of principle on temperature data indicates
that it might also be possible to improve foreground polarization templates
from the same input data.
