  The thermo-mechanical response of shock-initiated energetic materials (EM) is
highly influenced by their microstructures, presenting an opportunity to
engineer EM microstructure in a "materials-by-design" framework. However, the
current design practice is limited, as a large ensemble of simulations is
required to construct the complex EM structure-property-performance linkages.
We present the Physics-Aware Recurrent Convolutional (PARC) Neural Network, a
deep-learning algorithm capable of learning the mesoscale thermo-mechanics of
EM from a modest number of high-resolution direct numerical simulations (DNS).
Validation results demonstrated that PARC could predict the themo-mechanical
response of shocked EM with a comparable accuracy to DNS but with notably less
computation time. The physics awareness of PARC enhances its modeling
capabilities and generalizability, especially when challenged in unseen
prediction scenarios. We also demonstrate that visualizing the artificial
neurons at PARC can shed light on important aspects of EM thermos-mechanics and
provide an additional lens for conceptualizing EM.
