AI4DT&CP

The availability of easy-to-deploy sensors and the general advances in the Internet of Things (IoT) technology have led to the emergence of new applications that seamlessly blend the physical and digital worlds. Notwithstanding this trend, there are still open issues. A major one, due to the heterogeneity of the several models involved, is dealing with the complexity of the physical world to develop and deploy intelligent services that continuously perceive and learn from data coming from the environment.

The idea gained traction among both academic institutions and industry players, revitalising the Digital Twin technology that enables the creation of virtual replicas of physical objects by mirroring their properties, data and behaviors and enabling new intelligent and augmented functionalities such as modelling, simulation, and cognitive capabilities.

We believe that Artificial Intelligence (AI) will transform the field of Digital Twin technology by enabling the creation of intelligent virtual replicas that may offer smart services and lead to adaptive AI in cyber-physical environments.

Overall, AI can be used to enhance the performance, safety, and security of Digital Twin and cyber-physical systems by making them more intelligent, adaptive, and autonomous. The results can be better control, optimisation, and prediction of the Cyber-Physical systems. Digital Twin and cyber-physical systems can be enhanced with AI in several ways since AI enables real-time monitoring and control of physical systems with the possibility of delivering intelligent services with applications in several domains, such as:

• Predictive modelling: AI-powered digital twins can predict the behaviour of physical systems under different conditions, helping to identify potential issues or inefficiencies in the physical system before they occur.

• Anomaly detection: AI-powered digital twins can analyse sensor data from the physical system in real-time, using machine learning techniques to identify anomalies or deviations from normal behaviour.

• Digital Human Replica: Building virtual replicas of humans that reproduce and model both outer and inner aspects of a human being, such as physical and physiological characteristics, personality, sensitivities, thoughts and skills.

• Optimisation: AI-powered digital twins can analyse sensor data and other inputs to optimise the performance of the physical system (e.g., by adjusting the control parameters to minimise energy consumption or maximise production efficiency).

• Autonomous control: AI-powered digital twins can be used to control a physical system autonomously, using sensor data and other inputs to make real-time decisions.

• Safety and security: AI-powered digital twins can be used to monitor and analyse sensor data to detect security threats or unsafe conditions in the physical system and to trigger appropriate responses.

• Big Data Applications: Big data frameworks can be leveraged to make sense of huge data collections and provide the digital twins with further features, analysis, graphs.

At the same time, incorporating machine learning models into digital twin systems can be critical when monitoring or controlling critical systems.

Machine Learning Operations (MLOps) approaches are attracting increasing interest to ensure that intelligent models are deployed robustly and reliably, especially when exploiting Continual Learning or Reinforcement Learning techniques.

The workshop aims to bring together experts in the fields of Artificial Intelligence, Digital Twin technology, and Cyber-Physical systems to explore the latest developments and best practices in the use of AI-based digital twins for a wide range of cyber-physical services and applications.

Topics of interest of AI4DT&CP include, but are not limited to, the following:

• What-if scenarios with Cyber-Physical applications

• MLOps in Cyber-Physical systems

• Digital Twin intelligence management

• Digital Twins modelling for AI for physical augmentation

• Cyber-Physical application and MLOps for anomaly detection

• Digital Twins for synthetic data generation in Cyber-Physical applications

• Predictive Maintenance in Cyber-Physical systems

• Intelligent Digital Twins for optimisation use cases (Smart cities, smart buildings, environmental monitoring)

• Digital human replica with AI

• Cyber-Physical application with AI in healthcare

• Digital Twins for continual learning scenarios

• Reinforcement Learning in Cyber-Physical applications