Machine learning for predicting the outcome of terminal ballistics events
Shannon Ryan, Neeraj Mohan Sushma, Arun Kumar AV, Julian Berk, Tahrima Hashem, Santu Rana, Svetha Venkatesh
Abstract
Machine learning (ML) is well suited for the prediction of high-complexity, high-dimensional problems such as those encountered in terminal ballistics. We evaluate the performance of ML-based regression models on two common terminal ballistics’ problems: (a) predicting the V50 ballistic limit of monolithic metallic armour impacted by small and medium calibre projectiles and fragments, and (b) predicting the depth to which a projectile will penetrate a target of semi-infinite thickness. The models provide excellent agreement with test data. Although extrapolation is not advisable for ML-based regression models, for applications such as lethality/survivability analysis, such capability is required. To enable extrapolation, we implement expert knowledge and physics-based models via enforced monotonicity, as a Gaussian prior mean, and through a modified loss function. The physics-informed models demonstrate improved performance over both classical physics-based models and the basic ML regression models, providing an ability to accurately fit experimental data when it is available and then revert to the physics-based model when not.