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

Foundations of Digital Twins

• Key concepts and the historical evolution of digital twins
• Practical applications in manufacturing, energy, and logistics sectors
• Structural architecture and the complete lifecycle of a digital twin

Modeling Systems and Simulation

• Replicating dynamic systems using Simulink
• Comparing physics-based approaches against data-driven methods
• Visualizing system interactions with Unity

Integrating Real-Time Data

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

AI and Machine Learning in Digital Twins

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

Visualization and Dashboard Design

• Creating user interfaces for monitoring digital twins
• Exploring 3D and 2D visualization possibilities
• Building custom dashboards that deliver real-time insights

Case Study: Developing a Digital Twin Prototype

• End-to-end design process for a manufacturing asset twin
• Setting up data integration and machine learning pipelines
• Testing and deploying the solution in a simulated environment

Sustaining and Scaling Digital Twins

• Managing the lifecycle and applying necessary updates
• Ensuring interoperability and adhering to industry standards
• Expanding the digital twin to cover multiple assets or processes

Conclusion and Future Pathways