This article proposes new modeling methods for deterministic computer experiments with multiple tuning parameters. Finite Element Analysis (FEA) is commonly applied in large-scale computer experiments. Therefore, tuning parameters which control the mesh densities of the simulation are critical to its accuracy. This work utilizes a non-stationary Gaussian process model to bring together simulations of different mesh densities and improve the overall prediction performance. It looks at the case where there are more than one mesh density variables, which has not been investigated before. We apply the proposed method to an analytical function, a stress analysis for a beam structure, and a delamination analysis, which demonstrates its superior performance over the existing methods.