Computer experiments simulate the engineering systems by implementing the mathematical models governing the systems in computers. Recently, experiments having large number of input variables and experimental runs started to emerge. In the existing literature, kriging has been commonly used for approximating the complex computer models, but it has limitations for dealing with the large-scale experiments due to its computational complexity and numerical stability. In this talk, we present three new modeling approaches: regression-based inverse distance weighting (RIDW), Kernel approximation, and Approximate Kriging. The proposed methods are shown to be computationally more efficient and numerically more stable than kriging while producing comparable prediction performance.