  Feature related particle data analysis plays an important role in many
scientific applications such as fluid simulations, cosmology simulations and
molecular dynamics. Compared to conventional methods that use hand-crafted
feature descriptors, some recent studies focus on transforming the data into a
new latent space, where features are easier to be identified, compared and
extracted. However, it is challenging to transform particle data into latent
representations, since the convolution neural networks used in prior studies
require the data presented in regular grids. In this paper, we adopt Geometric
Convolution, a neural network building block designed for 3D point clouds, to
create latent representations for scientific particle data. These latent
representations capture both the particle positions and their physical
attributes in the local neighborhood so that features can be extracted by
clustering in the latent space, and tracked by applying tracking algorithms
such as mean-shift. We validate the extracted features and tracking results
from our approach using datasets from three applications and show that they are
comparable to the methods that define hand-crafted features for each specific
dataset.
