A Sequential Latin Hypercube Sampling Method for Metamodeling

For utilizing the original sampling points and improving budget allocation efficiency, we propose a sequential Latin Hypercube Sampling (LHS) method for metamodeling. The sequential method starts with an original LHS of size n and constructs a new LHS of size kn that has the original points as more as possible. The sampling points of LHS are described by some matrixes and it is proved that there is no need to delete original points for the new LHS. A subtraction rule is applied for adding new sampling points. The original and addition sampling points are proved to be a strict LHS. The method is applied for metamodeling to demonstrate the effectiveness.

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