For the latest version of JMP Help, visit JMP.com/help.


Design of Experiments Guide > Space-Filling Designs
Publication date: 06/21/2023

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. Space filling designs include sphere packing Latin hypercube, uniform, minimum potential, maximum entropy, Gaussian process, and fast flexible designs.

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 multiple design types for situations with only continuous factors. A special design type exists for designs that include categorical, discrete numeric or mixture factors. For continuous factors, space-filling designs have two objectives:

maximize the distance between any two design points

space the points uniformly

Figure 22.1 Space-Filling Design 

Space-Filling Design

Contents

Overview of Space-Filling Designs

Example of a Space-Filling Design

Build a Space-Filling Design

Responses
Factors
Define Factor Constraints
Space-Filling Design Methods
Design
Design Diagnostics
Design Table

Space Filling Design Options

Additional Examples of Space Filling Designs

Example of Creating a Latin Hypercube Design
Example of Creating a Uniform Design
Example Comparison of Sphere-Packing, Latin Hypercube, and Uniform Methods
Example of a Minimum Potential Design
Example of a Constrained Fast Flexible Filling Design
Example of a Space-Filling Design for a Map Shape
Example of a Sphere-Packing Design

Statistical Details for Space Filling Designs

Minimum Potential
Maximum Entropy
Gaussian Process IMSE
Fast Flexible Filling Design Details
Want more information? Have questions? Get answers in the JMP User Community (community.jmp.com).