PROGRAMME / 03
Master · AI Simulation and Digital Twins
Combine simulation, operational data and AI to build digital twins for monitoring, scenario testing, predictive maintenance and optimisation.
For applicants with relevant prior study. Develop advanced technical and research skills. Planned workload: 120 credits.
YEAR 01Complex Systems and Co-Simulation
- Co-Simulation
- Multi-Agent Systems
- Advanced System Identification
- Parallel Computing
- Data Platforms
- Research Methods
YEAR 02Intelligent Decisions and Twin Engineering
- Real-Time Simulation
- Digital Twin Lifecycle Management
- Simulation Credibility
- Reinforcement Learning and Optimisation
- Simulation-Based Decision Making
- Master’s Dissertation and Defence
SHARED LEARNING
Shared foundations. Specialist depth.
Share modelling, AI, data interfaces and cloud deployment modules, connecting them through specialist projects.
Specialist focus: Dynamical systems, co-simulation and continuously updated digital twins.
Assessment and outcomes
Assess model fidelity, updating mechanisms and prediction quality. Distinguish visualisation, offline simulation and dynamic digital twins, documenting data sources and update frequency.
Build a digital twin prototype using simulated, public or partner data, demonstrating state updates, scenario testing and prediction.