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

Space-filling designs are commonly used in deterministic computer experiments. However, they cannot do factor screening without any modeling assumptions, which makes them inefficient when only a small subset of input factors is important. Recently developed screening designs, such as MOFAT designs, can identify important factors without a modeling step 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.