To develop a more generic surrogate, the model-free sampling strategy can be more advantageous because it can guarantee randomness. In this sense, we should consider homogeneous sampling methods, such as Latin hypercube sampling (LHS), Sobol Sampling, and Monte Carlo sampling. Meanwhile, exploration should be well balanced with exploitation (model-free type) during sequential sampling. On the one hand, exploration samples the entire design space homogeneously. On the other hand, exploitation focuses on sampling the complex/nonlinear space. A general strategy is to divide the overall design space into several sub-spaces, and then rank them based on certain score criteria, following by exploiting the subspace with the worst score. Since it is out of the scope of this work, more detailed information can be referred to in the cited literature. Overall, prior studies focus more on the sampling methods, but little attention is given to reducing the total time for data generation. In this work we aim to obtain good-enough surrogate models via efficient sequential data generation. The efficiency benefits from: