Abstract
A metamodel is a simplified mathematical description of a simulation model that represents the system’s input–output relationship with a function. In many situations, we may not need a single formula to describe the systems being simulated. Interpolation-based metamodels are useful for providing simple estimates at non-design points to communicate the input–output relationship. This paper proposes a new approach to select an experimental design for interpolation-based metamodels. The algorithm dynamically increases the sample size and the number of design points so that the estimates obtained via the metamodel satisfy the pre-specified precision. An experimental performance evaluation demonstrates the validity of interpolation-based metamodels.
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