In this paper we expand on a previously presented method to autonomously create a reduced model to describe aspects of a computational fluid dynamics (CFD) simulation. We are trying to reduce the particle tracking of the CFD simulation so that we can determine the focal points of each particle simply by its initial injection parameters. In order to do so autonomously we utilize a Gaussian process (GP) which has the main advantages of also being able to depict random effects through a confidence of the model. While we exemplify this method through the case of the particle tracks in our Aerosol-on-Demand (AoD) jet-printhead, we see the CFD model as the ground truth for the purposes of validation. Our goal is to have the reduced model be as close as possible to the computationally expensive CFD simulation so that the reduced model can be used inline in future use and rapid iteration to improve the printing process.