  Studying the ambient solar wind, a continuous pressure-driven plasma flow
emanating from our Sun, is an important component of space weather research.
The ambient solar wind flows in interplanetary space determine how solar storms
evolve through the heliosphere before reaching Earth, and especially during
solar minimum are themselves a driver of activity in the Earth's magnetic
field. Accurately forecasting the ambient solar wind flow is therefore
imperative to space weather awareness. Here we present a machine learning
approach in which solutions from magnetic models of the solar corona are used
to output the solar wind conditions near the Earth. The results are compared to
observations and existing models in a comprehensive validation analysis, and
the new model outperforms existing models in almost all measures. In addition,
this approach offers a new perspective to discuss the role of different input
data to ambient solar wind modeling, and what this tells us about the
underlying physical processes. The final model discussed here represents an
extremely fast, well-validated and open-source approach to the forecasting of
ambient solar wind at Earth.
