Building Digital Twins with AI and Real-Time Data Training Course

Introduction to Digital Twins

• Concepts and the evolution of digital twins
• Applications in manufacturing, energy, and logistics
• Digital twin architecture and lifecycle

System Modelling and Simulation

• Modelling dynamic systems with Simulink
• Physics-based versus data-driven modelling
• Visualising systems with Unity

Real-Time Data Integration

• Utilising MQTT and OPC-UA for connectivity
• Streaming data with Node-RED
• Ingesting sensor and machine data into the twin

AI and Machine Learning in Digital Twins

• Integrating AI models for prediction and optimisation
• Using TensorFlow or PyTorch with live data
• Training models on simulation outputs

Visualisation and Dashboards

• Designing user interfaces for twin monitoring
• 3D and 2D visualisation options
• Custom dashboards with real-time insights

Case Study: Creating a Digital Twin Prototype

• End-to-end design of a manufacturing asset twin
• Data integration and machine learning setup
• Deployment and testing in a simulated environment

Maintaining and Scaling Digital Twins

• Lifecycle management and updates
• Interoperability and standards
• Scaling to multiple assets or processes

Summary and Next Steps