Space-Filling Designs

Space-filling designs are useful in situations where run-to-run variability is of far less concern than the form of the model. Consider a sensitivity study of a computer simulation model. In this situation, and for any mechanistic or deterministic modeling problem, any variability is small enough to be ignored. For systems with no variability, replication, randomization, and blocking are irrelevant.

The Space Filling platform provides designs for situations with both continuous and categorical factors. For continuous factors, space-filling designs have two objectives:

• maximize the distance between any two design points

• space the points uniformly

Figure 21.1 Space-Filling Design 

Contents

Overview of Space-Filling Designs

Build a Space Filling Design

Responses

Factors

Define Factor Constraints

Space Filling Design Methods

Design

Design Diagnostics

Design Table

Space Filling Design Options

Sphere-Packing Designs

Creating a Sphere-Packing Design

Visualizing the Sphere-Packing Design

Latin Hypercube Designs

Creating a Latin Hypercube Design

Visualizing the Latin Hypercube Design

Uniform Designs

Comparing Sphere-Packing, Latin Hypercube, and Uniform Methods

Minimum Potential Designs

Maximum Entropy Designs

Gaussian Process IMSE Optimal Designs

Fast Flexible Filling Designs

FFF Optimality Criterion

Set Average Cluster Size for FFF Designs

Constraints

Creating and Viewing a Constrained Fast Flexible Filling Design

Creating a Space-Filling Design for a Map Shape

Example of a Sphere-Packing Design

Create the Sphere-Packing Design for the Borehole Data

Guidelines for the Analysis of Deterministic Data