Meta models in Computer Experiments: Kriging versus Artificial Neural Networks
Non stochastic simulation models, such as finite element or computational fluid dynamics, often support real experiments in industrial research. It has become a common practice to provide a meta model as computer experiments can be highly complex and time consuming, and the design space is often broad. The meta model is an approximation of the computer experiments response adapted both globally and locally on the design space, in order to capture local minima/maxima. The Kriging model, first proposed in Geostatistics, is doubtlessly the most popular meta model because of its recognized ability to provide high quality predictions. The underlying correlation structure can be evaluated either by estimating the parameters of correlation or by means of a variogram. In this paper, the performance of the Kriging model is compared with an Artificial Neural Network meta model in order to determine which model guarantees higher accuracy in predicting the result of four dimensional com putational fluid dynamics experiments for low pressure turbines where energy loss values are provided
Qual. Reliab. Eng. Int.