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

Introduction to Digital Twins

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

System Modeling and Simulation

• Modeling dynamic systems using Simulink
• Comparing physics-based versus data-driven modeling approaches
• Visualizing systems with Unity

Real-Time Data Integration

• Establishing connectivity using MQTT and OPC-UA
• Streaming data via Node-RED
• Ingesting sensor and machine data into the twin environment

AI and Machine Learning in Digital Twins

• Integrating AI models for prediction and optimization tasks
• Leveraging TensorFlow or PyTorch with live data
• Training models based on simulation outputs

Visualization and Dashboards

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

Case Study: Developing a Digital Twin Prototype

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

Maintaining and Scaling Digital Twins

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

Summary and Future Steps