Gaussian Process Modelling of the F-16 Ground Vibration Test Benchmark: Data Selection Case Study

Abstract

Abstract We observe the effects of training data sample selection in modelling of a physical system with Gaussian process nonlinear autoregressive models with exogenous input. Gaussian process modelling limits the number of training data points and we use a big nonlinear benchmark data set. The combination calls for training data sample selection. We compare a ‘smart’ method based on Euclidean distance between training data points with decimation. We use the training data samples obtained by both methods to train the models, test model predictions on a test data set, and calculate two figures of merit, eRMSt and mean standardised log loss (MSLL). The model trained on the ‘smartly’ selected training data points is better in eRMSt while the one with the decimated data is superior in MSLL. The direct conclusion is that the purpose of the model determines which training data sample selection method is better, as the relevant figure of merit depends on the model purpose. We notice that the predicted variance is more sensitive to the training data sample than the predicted mean. We warn that training data sample selection may have unexpected consequences.