An adaptive sequential experiment design method for model validation

Efficient experiment design is of great significance for the validation of simulation model with high nonlinearity and large input space. Excessive validation experiment raises the cost while insufficient test increases the risks of accepting an invalid model. In this paper, an adaptive sequential experiment design method combining global exploration criterion and local exploitation criterion is proposed. The exploration criterion utilizes discrepancy metric to improve the space-filling property...

An adaptive sequential experiment design method for model validation

Efficient experiment design is of great significance for the validation of simulation model with high nonlinearity and large input space. Excessive validation experiment raises the cost while insufficient test increases the risks of accepting an invalid model. In this paper, an adaptive sequential experiment design method combining global exploration criterion and local exploitation criterion is proposed. The exploration criterion utilizes discrepancy metric to improve the space-filling property of the design points while the exploitation criterion employs the leave one out error to discover informative points. To avoid the clustering of samples in the local region, an adaptive weight updating approach is provided to maintain the balance between exploration and exploitation. Besides, the credibility distribution function characterizing the relationship between the input and result credibility is introduced to support the model validation experiment design. Finally, six benchmark problems and an engineering case are applied to examine the performance of the proposed method. The experiments indicate that the proposed method achieves satisfactory performance for function approximation in accuracy and convergence.