Design of Experiments for Emulations: A Review

Music City Center 

Space-filling designs are crucial for efficient computer experiments, enabling accurate surrogate modeling and uncertainty quantification in many scientific and engineering applications, such as digital twin systems and cyber-physical systems.
In this work, we will provide a comprehensive review on key design methodologies, including Maximin/miniMax designs, Latin hypercubes, and projection-based designs. Moreover, we will connect the space-filling design criteria like the fill distance to Gaussian process performance.
Numerical studies are conducted to investigate the practical trade-offs among various design types, with the discussion on emerging challenges in high-dimensional and constrained settings.
The paper concludes with future directions in adaptive sampling and machine learning integration, providing guidance for improving computational experiments.

computer experiment

design of experiment

emulation

surrogate models