The article discusses a method to optimize engineering designs more efficiently by using a combination of Latin Hypercube design of experiment and Kriging modeling. This method helps reduce the number of costly experiments needed by creating a surrogate model that predicts design performance accurately. The researchers introduced an adaptive sampling technique that further improves the accuracy of predictions, especially for complex design problems. The approach showed promising results in enhancing design performance and has the potential to uncover optimal configurations in challenging scenarios.