  The sensitivity of heterogeneous energetic (HE) materials (propellants,
explosives, and pyrotechnics) is critically dependent on their microstructure.
Initiation of chemical reactions occurs at hot spots due to energy localization
at sites of porosities and other defects. Emerging multi-scale predictive
models of HE response to loads account for the physics at the meso-scale, i.e.
at the scale of statistically representative clusters of particles and other
features in the microstructure. Meso-scale physics is infused in
machine-learned closure models informed by resolved meso-scale simulations.
Since microstructures are stochastic, ensembles of meso-scale simulations are
required to quantify hot spot ignition and growth and to develop models for
microstructure-dependent energy deposition rates. We propose utilizing
generative adversarial networks (GAN) to spawn ensembles of synthetic
heterogeneous energetic material microstructures. The method generates
qualitatively and quantitatively realistic microstructures by learning from
images of HE microstructures. We show that the proposed GAN method also permits
the generation of new morphologies, where the porosity distribution can be
controlled and spatially manipulated. Such control paves the way for the design
of novel microstructures to engineer HE materials for targeted performance in a
materials-by-design framework.
