Machine learning methods for predicting the outcome of hypervelocity impact events
Two machine learning techniques are applied to characterize hypervelocity impacts. Both techniques offered improved accuracy over conventional semi-analytical methods. Further improvements are restricted by the statistical quality of the training data. A novel approach is proposed for identifying gaps in the training dataset. This approach is successfully demonstrated to improve the network predictions.
hypervelocity impact artificial neural network support vector machine terminal ballistics