Spatio-temporal Feature Exploration in Combined Particle/Volume Reference Frames

The use of large-scale scientific simulations that can represent physical systems using both particle and volume data simultaneously is gaining popularity as each of these reference frames has an inherent set of advantages when studying different phenomena. Furthermore, being able to study the dynamic evolution of these time varying data types is an integral part of nearly all scientific endeavors. However, the techniques available to scientists generally limit them to studying each reference frame separately making it difficult to draw connections between the two. In this work we present a novel method of feature exploration that can be used to investigate spatio-temporal patterns in both data types simultaneously. More specifically, we focus on how spatio-temporal subsets can be identified from both reference frames, and develop new ways of visually presenting the embedded information to a user in an intuitive manner. We demonstrate the effectiveness of our method using case studies of real world scientific datasets and illustrate the new types of exploration and analyses that can be achieved through this technique.