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

Introduction to Digital Twins

• Core concepts and the evolution of digital twins
• Applications in manufacturing, energy, and logistics
• Architecture and lifecycle of digital twins

System Modeling and Simulation

• Modeling dynamic systems using Simulink
• Physics-based versus data-driven modeling approaches
• System visualization with Unity

Real-Time Data Integration

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

AI and Machine Learning in Digital Twins

• Integrating AI models for prediction and optimization
• Applying TensorFlow or PyTorch with live data
• Training models using simulation outputs

Visualization and Dashboards

• Designing user interfaces for twin monitoring
• 3D and 2D visualization capabilities
• Creating custom dashboards with real-time insights

Case Study: Developing a Digital Twin Prototype

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

Maintaining and Scaling Digital Twins

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

Summary and Future Steps