Our journal article on AI Simulation by Digital Twins: Systematic Survey, Reference Framework, and Mapping to a Standardized Architecture with Xiaoran (Sharon) Liu (PhD student in our lab) has been accepted for publication in the Journal of Software and Systems Modeling (SoSyM).
This is article is an invited extension of Sharon’s conference paper at EDTConf 2024 and provides insights into the state of the art on AI simulation by digital twins, defines a reference framework to situate AI and digital twin components in software systems, and provides architectural and implementational pointers through the ISO 23247 reference architecture.
Preprint: available on arXiv.
Abstract. Insufficient data volume and quality are particularly pressing challenges in the adoption of modern subsymbolic AI. To alleviate these challenges, AI simulation uses virtual training environments in which AI agents can be safely and efficiently developed with simulated, synthetic data. Digital twins open new avenues in AI simulation, as these high-fidelity virtual replicas of physical systems are equipped with state-of-the-art simulators and the ability to further interact with the physical system for additional data collection. In this article, we report on our systematic survey of digital twin-enabled AI simulation. By analyzing 22 primary studies, we identify technological trends and derive a reference framework to situate digital twins and AI components. Based on our findings, we derive a reference framework and provide architectural guidelines by mapping it onto the ISO 23247 reference architecture for digital twins. Finally, we identify challenges and research opportunities for prospective researchers.