Gaussian processes for shock test emulation

Abstract Certifying performance of mechanical components with experimental tests is time consuming and expensive, which motivates the development of efficient approaches for predicting the outcomes from such testing. We propose two methods based on Gaussian processes (GP) to estimate the probability that new components will pass future certification tests, while assessing our prediction confidence. The first method processes a set of Bernoulli trials into a suitable machine learning dataset and subsequently infers the probability of performing satisfactorily for new components using heteroscedastic bounded GP regression. The second method uses GP classification with linear kernels. We demonstrate that linear kernels are well suited for datasets representing snapshots of mechanical system responses by accurately reproducing the underlying physical trends in the data. This yields consistent probabilities of passing and provides high labeling accuracy, even with small datasets. We demonstrate these techniques on synthetic datasets consistent with ship cabinet certification tests. We achieve up to 100% accuracy using all of the training data, and at least 92% with only 10% of the available data. With a corrupted training set, we obtain at least 93% accuracy. In the regression framework, we demonstrate that introducing heteroscedasticity helps achieve significantly better accuracy than frequentist machine learning methods.