Digital twins have emerged as transformative tools that allow the coupling of computational and physical assets. Their role can be of critical importance in both design and optimisation, as well as real-time decision making and control. A digital twin is typically enabled by two core components, data and simulators, which need to accommodate its requirement for efficiency, accuracy and in various cases adaptability. The simulators can be physics-based, data-driven or hybrids. They can be fully coupled to the physical system, adaptive, or updatable. In any case, they need to be robust and reliable since they are critical for the successful development, application and deployment of a digital twin in an industrial setting. This session aims to gather contributions highlighting the use of physics-, data-, or hybrid-based simulators and their impact on digital twin applications. Contributors are invited to discuss topics ranging from, but not limited to physics-driven modelling, reduced order modelling, scientific machine learning, model adaptivity and updating, predictive maintenance, optimal sensing, monitoring and optimal control in energy systems.