A Calibration Method Based on Surrogate Model for Simulation Models with Multi-Variant Outputs

To solve the calibration problem of simulation model with multi-variant and different kinds of output data, a calibration method based on optimization and surrogate model was presented. To acquire the output of simulation model with both of aleatory and epistemic uncertainty, an uncertainty propagation method based on two stage nested latin hyper sample(LHS) was introduced. Then, a coherence measurement method based on data feature was used to measure the coherence of the simulation and reference outputs. Furthermore, a stochastic Kriging model was applied to build the data coherence surrogate model of the simulation output and epistemic uncertainty sample. And based on the surrogate model, the calibration results were obtained via the genetic algorithm. Finally, the method was validated in the application.