On the use of gradients in Kriging surrogate models

The use of Kriging surrogate models has become popular in approximating computation-intensive deterministic computer models. In this work, the effect of enhancing Kriging surrogate models with a (partial) set of gradients is investigated. While, intuitively, gradient information is useful to enhance prediction accuracy, another motivation behind this work is to see whether it is worth including the gradients versus their computation time. Test results of two analytical functions and a fluid-structure interaction (FSI) problem from bio-mechanics show that this approach, known as Gradient Enhanced Kriging (GEK), can significantly enhance the accuracy of Kriging models even when the gradient data is only partially available.