An integral model for high-accuracy and low-accuracy experiments

A growing trend in engineering and science is to use multiple computer codes with different levels of accuracy to study the same complex system. Strategies are needed to combine the simulation results obtained at different levels of accuracy to produce an efficient surrogate model for prediction. In this paper, we propose an integral model to borrow as much information as possible from the low-accuracy experiment. We ignore the Markov property assumed before and model the high-accuracy experiment based on an integral form of the low-accuracy experiment. The proposed model is more general thus better predictions are expected. Two explicit forms of some matrices and vectors used in our predictions are given. The effectiveness of the proposed model is illustrated with several examples.

Automated summary

This paper discusses the growing trend of using multiple computer codes with different levels of accuracy in engineering and science to study complex systems. It proposes an integral model that combines simulation results obtained at different levels of accuracy to produce better predictions. The effectiveness of the proposed model is demonstrated through several examples.