Space-Filling One-Factor-At-A-Time Designs

2026-06-01

AI Summary

This study addresses the challenge that existing factor screening designs struggle to simultaneously achieve high screening efficiency and adequate space-filling properties, thereby limiting the accuracy of subsequent surrogate modeling. To overcome this limitation, the authors propose a novel class of one-factor-at-a-time (OFAT) designs that systematically incorporates space-filling characteristics into the OFAT framework for the first time. While preserving the inherent efficiency of OFAT in identifying active factors, the proposed approach significantly enhances coverage of the input space. By refining the MOFAT family of designs through optimization-based space-filling criteria, the method demonstrates superior performance in both factor identification accuracy and space-filling quality across multiple numerical experiments, effectively balancing the dual objectives of efficient screening and high-fidelity modeling.

Abstract

Space-filling designs are commonly used in deterministic computer experiments. However, they are ineffective for factor screening, which makes them inefficient when only a small subset of input factors is influential to the output. Recently developed screening designs, such as MOFAT designs, are effective at identifying important factors but lack space-filling properties, limiting their usefulness for surrogate modeling. In this article, we propose a new class of screening designs that improves the space-fillingness while retaining their screening capability. Through several numerical examples, we demonstrate that the proposed designs offer clear advantages over existing designs.

Problem

• space-filling
• factor screening
• computer experiments
• surrogate modeling
• experimental design

Innovation

• space-filling
• factor screening
• MOFAT designs
• surrogate modeling
• computer experiments