  The rapid compaction of granular media results in localized heating that can
induce chemical reactions, phase transformations, and melting. However, there
are numerous mechanisms in play that can be dependent on a variety of
microstructural features. Machine learning techniques such as neural networks
offer a ubiquitous method to develop models for physical processes. Limiting
what kinds of microstructural information is used as input and assessing
normalized changes in network error, the relative importance of different
mechanisms can be inferred. Here we utilize binned, initial density information
as network inputs to predict local shock heating in a granular high explosive
trained from large scale, molecular dynamics simulations. The spatial extend of
the density field used in the network is altered to assess the importance and
relevant length scales of the physical mechanisms in play, where different
microstructural features result in different predictive capability.
