Abstract: Sample augmentation  could improve the accuracy of the Kriging models under small samples. It adds additional data to the original samples, the augmented samples are used to build the Kriging model. There are two methods to create the additional samples. The first one uses maximum likelihood estimation (MLE), the other uses minimum means squared error (MSE). The MLE process is too strict to provide enough additional useful information, the augmented samples are exceedingly close to the original samples. The minimum MSE process gets new samples with available information to the original database, the accuracy of the Kriging model under the augmented samples is improved. The minimum MSE process can solve the problem about constructing the high accuracy Kriging models under small samples. Finally, a torpedo shape design optimization was carried out with the technique, and the Kriging model using the minimum MSE process shows its high precision and reliability. 

Key words: Kriging interpolation, maximum likelihood estimation (MLE), means squared error (MSE), design optimization