  This work presents the procedure for constructing a machine learned surrogate
model for hotspot ignition and growth rates in pressed HMX materials. A
Bayesian Kriging algorithm is used to assimilate input data obtained from
high-resolution meso-scale simulations. The surrogates are built by generating
a sparse set of training data using reactive meso-scale simulations of void
collapse by varying loading conditions and void sizes. Insights into the
physics of void collapse and ignition and growth of hotspots are obtained. The
criticality envelope for hotspots is obtained as the function
{\Sigma}_cr=f(P_s,D_void ) where P_s is the imposed shock pressure and D_void
is the void size. Criticality of hotspots is classified into the plastic
collapse and hydrodynamic jetting regimes. The information obtained from the
surrogate models for hotspot ignition and growth rates and the criticality
envelope can be utilized in meso-informed Ignition and Growth (MES-IG) models
to perform multi-scale simulations of pressed HMX materials.

Computational Physics;Mesoscale and Nanoscale Physics