Key points are not available for this paper at this time.
Digital twins (DTs) have evolved from domain-specific simulation tools into integrative cyber–physical–social infrastructures that reshape how complex systems are observed, modeled, and governed. Rather than treating DTs as digital replicas, this paper conceptualizes them as dynamic epistemic architectures that integrate observation, physics-based modeling, AI, and decision processes through persistent bidirectional exchange. Drawing on a systematic review of 251 papers (from 449 screened abstracts within 22,434 publications, supplemented by foundational literature), we examine how this transition is enabled by advances in sensing, scalable computing, data assimilation, uncertainty quantification, and AI–physics integration. We argue that the defining feature of mature DTs is not replication fidelity alone, but their capacity to support uncertainty-aware, scenario-driven decision-making across scales, from engineered components to Earth system processes. DTs increasingly operate as system-of-systems (“DT-of-DTs”), requiring interoperable architectures, machine-readable metadata, and standardized trust frameworks to enable composability across organizational boundaries. This review synthesizes technological enablers, cross-domain applications, and emerging governance challenges, and identifies key research priorities in multiscale modeling, probabilistic inference, interoperability standards, certification pathways, and human-centered design. We conclude that the next generation of DTs will depend less on isolated technological advances and more on disciplined integration across physics, data science, computation, domain science, decision science, governance science and institutional practice. In this trajectory, DTs are not static models but evolving infrastructures for adaptive, resilient, and evidence-based decision support in complex socio-technical systems.
Yang et al. (Sun,) studied this question.