  Emanating from the base of the Sun's corona, the solar wind fills the
interplanetary medium with a magnetized stream of charged particles whose
interaction with the Earth's magnetosphere has space-weather consequences such
as geomagnetic storms. Accurately predicting the solar wind through
measurements of the spatio-temporally evolving conditions in the solar
atmosphere is important but remains an unsolved problem in heliophysics and
space-weather research.
  In this work, we use deep learning for prediction of solar wind (SW)
properties. We use Extreme Ultraviolet images of the solar corona from space
based observations to predict the SW speed from the NASA OMNIWEB dataset,
measured at Lagragian point 1. We evaluate our model against autoregressive and
naive models, and find that our model outperforms the benchmark models,
obtaining a best-fit correlation of 0.55 $\pm$ 0.03 with the observed data.
  Upon visualization and investigation of how the model uses data to make
predictions, we find higher activation at the coronal holes for fast wind
prediction ($\approx$ 3 to 4 days prior to prediction), and at the active
regions for slow wind prediction. These trends bear an uncanny similarity to
the influence of regions potentially being the sources of fast and slow wind,
as reported in literature. This suggests that our model was able to learn some
of the salient associations between coronal and solar wind structure without
built-in physics knowledge. Such an approach may help us discover hitherto
unknown relationships in heliophysics datasets.
