  Geomagnetic disturbance forecasting is based on the identification of solar
wind structures and accurate determination of their magnetic field orientation.
For nowcasting activities, this is currently a tedious and manual process.
Focusing on the main driver of geomagnetic disturbances, the twisted internal
magnetic field of interplanetary coronal mass ejections (ICMEs), we explore a
convolutional neural network's (CNN) ability to predict the embedded magnetic
flux rope's orientation once it has been identified from in situ solar wind
observations. Our work uses CNNs trained with magnetic field vectors from
analytical flux rope data. The simulated flux ropes span many possible
spacecraft trajectories and flux rope orientations. We train CNNs first with
full duration flux ropes and then again with partial duration flux ropes. The
former provides us with a baseline of how well CNNs can predict flux rope
orientation while the latter provides insights into real-time forecasting by
exploring how accuracy is affected by percentage of flux rope observed. The
process of casting the physics problem as a machine learning problem is
discussed as well as the impacts of different factors on prediction accuracy
such as flux rope fluctuations and different neural network topologies.
Finally, results from evaluating the trained network against observed ICMEs
from Wind during 1995-2015 are presented.
