Adaptive infill sampling strategy for metamodeling: Challenge and future research directions

The widespread use of computer experiments for design optimization has
made the issue of reducing computational cost, improving accuracy, removing the “curse of dimensionality” and avoiding expensive function approximation becoming even more important. Metamodeling also known as surrogate modeling, can approximate the actual simulation model allowing for much faster execution time thus becoming a useful method to mitigate these problems. There are two (2) well-known metamodeling techniques
which is kriging and radial basis function (RBF) discussed in this paper
based on widely used algorithm tool from previous work in modern engineering design of optimization. An integral part of metamodeling is in
the method to sample new data from the actual simulation model. Sampling
new data for metamodeling requires finding the location (or value) of one or
more new data such that the accuracy of the metamodel can be increased as
much as possible after the sampling process. This paper discussed the challenges of adaptive sampling in metamodel and proposed an ensemble
non-homogeneous method for best model voting to obtain new sample points.