Digital twins (DT) are emerging as an extremely promising paradigm for run-time modelling and performability prediction of cyber-physical systems (CPS) in various domains. Although several different definitions and industrial applications of DT exist, ranging from purely visual three-dimensional models to predictive maintenance tools, the focus on data-driven evaluation and prediction of critical dependability attributes such as safety are paramount. To that end, a conceptual framework based on autonomic systems to host DT run-time models based on a structured and systematic approach is and important feature. The convergence between DT and self-adaptation is the key to building smarter, resilient and trustworthy CPS that can self-monitor, self-diagnose and—ultimately—self-heal. The conceptual framework eases dependability assessment, which is essential for future certification of autonomous CPS operating with artificial intelligence and machine learning in critical applications.
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