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

Fundamentals of Digital Twins

• Theoretical concepts and the historical progression of digital twins
• Practical applications in manufacturing, energy, and logistics sectors
• Architectural frameworks and lifecycle stages

System Modelling and Simulation

• Representing dynamic systems utilizing Simulink
• Comparing physics-based approaches against data-driven modelling
• Visualising systems through Unity

Real-Time Data Integration

• Leveraging MQTT and OPC-UA for network connectivity
• Managing data streams with Node-RED
• Ingesting sensor and machine data into the digital twin

AI and Machine Learning Applications in Digital Twins

• Embedding AI models for predictive and optimisation tasks
• Utilizing TensorFlow or PyTorch alongside live data
• Training algorithms based on simulation results

Visualisation and Dashboard Creation

• Crafting user interfaces for twin monitoring
• Exploring 3D and 2D visualisation capabilities
• Building custom dashboards offering real-time insights

Case Study: Developing a Digital Twin Prototype

• Comprehensive design of a manufacturing asset twin
• Setting up data integration and machine learning pipelines
• Deployment and validation within a simulated environment

Upkeep and Scaling of Digital Twins

• Managing the lifecycle and applying updates
• Ensuring interoperability and adherence to standards
• Expanding across multiple assets or operational processes

Conclusions and Future Pathways