Extended kernel regression: A multi-resolution method to combine simulation experiments with analytical methods

Simulation is widely used to predict the performance of complex systems. The main drawback of simulation is that it is slow in execution and the related compute experiments can be very expensive. On the other hand, analytical methods are used to rapidly provide performance estimates, but they are often approximate because of their restrictive assumptions. Recently, Extended Kernel Regression (EKR) has been proposed to combine simulation with analytical methods for reducing the computational effort. This paper has different purposes. Firstly, EKR is tested on different cases and compared with other techniques. Secondly, two different methods for calculation of confidence band are proposed. Numerical results show that the EKR method provides accurate predictions, particularly when the computational effort is low. Results also show that the performance of the two confidence band methods depends on the case analyzed. Thus, further studies are necessary to develop a robust method for confidence band calculation.