Model Selection based on Regional Error Estimation of Surrogates

The analysis of complex system behavior often demands expensive experiments or computational simula-tions. Surrogate modeling techniques are often used to provide a tractable and inexpensive approximation of such complex system behavior. Owing to the lack of any general guidelines regarding the suitability of different surrogate models for different applications, model selection approach can be helpful to choose the best surrogate technique. This paper investigates the effectiveness of a recently developed method for surrogate error quantification called, Regional Error Estimation of Surrogate (REES), to select the best surrogate model based on the level of accuracy. The REES method is developed based on the concept that the accuracy of the approximation methods is related to the amount of available resources. In the REES method, intermediate surrogates are iteratively constructed over heuristic subsets of the available sample points (i.e., intermediate training points) and tested over the remaining available sample points (i.e., intermediate test points). The statistical mode of the median and the maximum error distributions are selected to represent the overall and maximum error at each iteration. The estimated modes of the median and maximum error distributions are then represented as functions of the number of interme-diate training points using a regression model. The regression models are used to predict the overal