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

Fundamentals of Digital Twins

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

System Modeling and Simulation Techniques

• Simulating dynamic systems using Simulink
• Comparing physics-based and data-driven modeling approaches
• System visualization utilizing Unity

Integrating Real-Time Data

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

AI and Machine Learning within Digital Twins

• Embedding AI models for predictive analytics and optimization
• Utilizing TensorFlow or PyTorch with live data feeds
• Training models based on simulation outputs

Visualization and Dashboard Design

• Crafting user interfaces for effective twin monitoring
• Exploring 3D and 2D visualization capabilities
• Building custom dashboards that provide real-time insights

Case Study: Developing a Digital Twin Prototype

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

Sustaining and Scaling Digital Twins

• Lifecycle management and continuous updates
• Ensuring interoperability and adherence to standards
• Scaling solutions across multiple assets or processes

Conclusion and Future Steps