A method for the updating of stochastic kriging metamodels

Two standard approaches to predicting the expected values of simulation outputs are either execution of the simulation itself or the use of a metamodel. In this work we propose a methodology that enables both approaches to be combined. When a prediction for a new input is required the procedure is to augment the metamodel forecast with additional simulation outputs for a given input. The key benefit of the method is that it is possible to reach the desired prediction accuracy at a new input faster than in the case when no initial metamodel is present. We show that such a procedure is computationally simple and can be applied to, for instance, web-based simulations, where response time to user actions is often crucial.