Year: 2026
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Yu, Wei-Yang, Joseph, V. Roshan
Institution: Georgia Institute of Technology
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W7163129127