PROGRAMME / 03
Bachelor · AI Simulation and Digital Twins
Combine simulation, operational data and AI to build digital twins for monitoring, scenario testing, predictive maintenance and optimisation.
Progress from foundations to professional practice. Planned workload: 180 credits.
YEAR 01Computing and Systems Foundations
- Common Foundation Modules
- Introduction to System Representation
- Data Visualisation
- Databases
- Programming and Algorithms
- Interactive Prototype Project
YEAR 02System Modelling and Data Integration
- System Modelling
- Discrete-Event Simulation
- Numerical Computing
- Machine Learning
- Networks and Data Interfaces
- Sensor Data Processing
YEAR 03Digital Twin Development
- Dynamical Systems and Model Calibration
- Digital Twin Architecture
- State Estimation
- Real-Time Data Integration
- Predictive Maintenance
- Digital Twin Capstone
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.