Design and Analysis for Digital Twin Optimization

ABSTRACT Digital twins (DTs) provide dynamic, virtual representations of physical systems, facilitating real‐time monitoring, prediction, and optimization through the integration of sensor data, computational models, and feedback mechanisms. Despite their potential, deploying DTs in complex systems often entails substantial computational costs. To address this challenge, recent studies have introduced surrogate models—referred to as digital triplets—as computationally efficient approximations of DTs. In particular, a framework that combines Gaussian process (GP) regression with the maximum projection (MaxPro) designs has shown promising predictive performance. However, the impact of alternative surrogate modeling techniques and the sequential experiments on optimization accuracy and convergence remains insufficiently understood. This study systematically evaluates a range of model–design combinations, including GP regression, polynomial regression, neural networks, and automated machine learning, in conjunction with central composite, optimal, MaxPro, and space‐filling designs. Performance is assessed in both two‐ and six‐dimensional DT scenarios, with metrics focused on optimization accuracy and convergence behavior. The findings reaffirm the strong performance of the MaxPro–GP combination, particularly in settings with moderate simulation budgets, thereby extending existing insights in the literature.