In experiments on systems where there is substantial random noise, the goal is to minimize the variance of prediction. In experiments on deterministic systems, there is no variance but there is bias. Bias is the difference between the approximation model and the true mathematical function. The goal of space-filling designs is to bound the bias.
The Space Filling designer supports the following design methods:
maximizes the minimum distance between pairs of design points. See Sphere-Packing Designs and Create the Sphere-Packing Design for the Borehole Data.
maximizes the minimum distance between design points but requires even spacing of the levels of each factor. This method produces designs that mimic the uniform distribution. The Latin Hypercube method is a compromise between the Sphere-Packing method and the Uniform design method. See Latin Hypercube Designs.
minimizes the discrepancy between the design points (which have an empirical uniform distribution) and a theoretical uniform distribution. See Uniform Designs.
measures the amount of information contained in the distribution of a set of data. See Maximum Entropy Designs.
creates a design that minimizes the integrated mean squared error of the Gaussian process over the experimental region. See Gaussian Process IMSE Optimal Designs.
The Fast Flexible Filling method forms clusters from random points in the design space. These clusters are used to choose design points according to an optimization criterion. This is the only method that can accommodate categorical factors and constraints on the design space. You can specify linear constraints and disallowed combinations. See Fast Flexible Filling Designs and Creating and Viewing a Constrained Fast Flexible Filling Design.