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 explores how to apply Kriging to random simulations. The researchers also discuss using bootstrapping to estimate prediction accuracy. Additionally, the article covers different sampling methods for sensitivity analysis and optimization. The study concludes by suggesting areas for future research in this field.