Simulation-Based Digital Twins: An Accreditation Method

The simulation-based Digital Twin (DT) has been gaining prominence in recent years and represents a revolution in decision-making processes. In this context, increasingly fast and efficient decisions are made by mirroring the behavior of physical systems and using advanced analysis techniques. On the other hand, this article draws attention to the challenges of ensuring the accreditation of simulation models over time, as traditional approaches do not consider the periodic updating of the model. Therefore, this work proposes an approach based on the periodic evaluation of these models using Machine Learning and control chart. More specifically, the authors adopted the K-Nearest Neighbors (K-NN) classifier, combined with a p-chart. The proposed approach has been tested in theoretical and real case studies, allowing us to monitor the DT results and ensuring its accreditation. The broad applicability of the proposed tool is highlighted, which can be used in simulation-based DTs with different characteristics.