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

Foundamentals of Digital Twins

• Core concepts and the evolution of digital twins
• Application examples in manufacturing, energy, and logistics
• Architectural components and lifecycle management

System Modeling and Simulation Techniques

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

Integrating Live Data

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

Applying AI and Machine Learning to Digital Twins

• Embedding AI models for predictive analysis and optimization
• Utilizing TensorFlow or PyTorch with live data feeds
• Training models using simulation results

Visualization and Dashboard Design

• Creating user interfaces for monitoring twins
• Exploring 3D and 2D visualization capabilities
• Developing custom dashboards with live 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
• Testing and deploying in a simulated context

Maintenance and Scalability of Digital Twins

• Managing lifecycle updates and maintenance
• Ensuring interoperability and adhering to standards
• Expanding to multiple assets or processes

Wrap-up and Future Directions