Physics-informed Machine Learning Models for Go/No-go Criteria on Reactive Metamaterials

We present a physics-informed machine learning framework for predicting Go/No-Go criteria for reactive metamaterials and study shock propagation through a one-dimensional laminate structure. The laminate material was composed of an HMX bed with equally distributed 2mm thick copper pillars. The Wide-Ranging equation of state (WR EOS) was used to model HMX while the Romenski EOS was used for the elastic regime of copper, with the assumption of perfect plasticity. The shock was initiated by using an aluminum impactor and gauges were placed at the entry of the first copper pillar and exit of the last pillar. A modified machine learning model was then developed to predict the Go/No-Go criteria for the laminate structure. The proposed model only uses short-time measurements for predicting this behavior, that leads to large reductions in computational cost at higher dimensions. This framework suggests a data-driven guideline for the design of optimal laminate structures (e.g. number of copper pillars, thickness, and distribution).