The article reviews a method called Kriging, which helps predict outcomes in simulations by considering spatial relationships. It compares Kriging to traditional linear regression models and discusses how to apply Kriging to random simulations. The article also explores ways to estimate the accuracy of Kriging predictions and different sampling methods for sensitivity analysis and optimization. Finally, it suggests areas for future research in this field.