  In this work, we pose the question of whether, by considering qualitative
information such as a sample target image as input, one can produce a rendered
image of scientific data that is similar to the target. The algorithm resulting
from our research allows one to ask the question of whether features like those
in the target image exists in a given dataset. In that way, our method is one
of imagery query or reverse engineering, as opposed to manual parameter
tweaking of the full visualization pipeline. For target images, we can use
real-world photographs of physical phenomena. Our method leverages deep neural
networks and evolutionary optimization. Using a trained similarity function
that measures the difference between renderings of a phenomenon and real-world
photographs, our method optimizes rendering parameters. We demonstrate the
efficacy of our method using a superstorm simulation dataset and images found
online. We also discuss a parallel implementation of our method, which was run
on NCSA's Blue Waters.
