Given that operational cyber-physical systems (CPS) produce continuous data, a complementary approach to model-based engineering is to learn digital twins models with machine learning techniques and providing functionalities such as predictions and anomaly detection.

This talk will start with presenting an opinion on the next generation of digital twins (Quantum Digital Twins), where some aspects of digital twins will be implemented as quantum software and executed on quantum computers, e.g., for simulating the physical environment that can be realistically simulated with quantum-mechanical principles.

Followed by this opinion, the talk will present some recent works on learning digital twins from historical data and continuous updates of digital twins with continuous data from operational CPS. Various machine learning techniques were applied, such as generative adversarial networks, curriculum learning, and transfer learning to learn digital twins. The digital twins were built for use cases from the transportation domain and water distribution/treatment plants. These digital twins were focused on anomaly detection and waiting time predictions.