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Design of Experiments Guide > Space-Filling Designs
Publication date: 11/10/2021

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 

Space-Filling Design


Overview of Space-Filling Designs

Build a Space Filling Design

Define Factor Constraints
Space Filling Design Methods
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
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
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