首页|Machine learning for predicting the outcome of terminal ballistics events
Machine learning for predicting the outcome of terminal ballistics events
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
万方数据
维普
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 four popular ML-based regression models,extreme gradient boosting(XGBoost),artificial neural network(ANN),support vector regression(SVR),and Gaussian process regression(GP),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.To achieve this we utilise two datasets,each consisting of approximately 1000 samples,collated from public release sources.We demonstrate that all four model types provide simi-larly excellent agreement when interpolating within the training data and diverge when extrapolating outside this range.Although extrapolation is not advisable for ML-based regression models,for appli-cations such as lethality/survivability analysis,such capability is required.To circumvent this,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 per-formance 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.The resulting models demonstrate high levels of predictive accuracy over a very wide range of projectile types,target materials and thicknesses,and impact conditions significantly more diverse than that achievable from any existing analytical approach.Compared with numerical analysis tools such as finite element solvers the ML models run orders of magnitude faster.We provide some general guide-lines throughout for the development,application,and reporting of ML models in terminal ballistics problems.
万方数据
维普
