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

Introduction to Digital Twins

• Core concepts and the evolution of digital twins
• Practical applications in manufacturing, energy, and logistics sectors
• Architectural frameworks and lifecycle management

System Modelling and Simulation

• Simulating dynamic systems using Simulink
• Comparing physics-based versus data-driven modelling approaches
• Visualising complex systems within Unity

Real-Time Data Integration

• Establishing connectivity using MQTT and OPC-UA protocols
• Managing data streams via Node-RED
• Ingesting sensor and machine data into the digital twin

AI and Machine Learning in Digital Twins

• Embedding AI models for prediction and operational optimisation
• Utilising TensorFlow or PyTorch with live data inputs
• Training models based on simulation outputs

Visualisation and Dashboards

• Designing intuitive user interfaces for twin monitoring
• Exploring 3D and 2D visualisation capabilities
• Building custom dashboards that provide real-time insights

Case Study: Developing a Digital Twin Prototype

• End-to-end design of a twin for a manufacturing asset
• Setting up data integration and machine learning pipelines
• Deployment and rigorous testing in a simulated environment

Maintaining and Scaling Digital Twins

• Managing lifecycle updates and maintenance
• Ensuring interoperability and adherence to industry standards
• Scaling solutions across multiple assets or processes

Summary and Future Pathways