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 sectors.
• Architectural design and lifecycle stages.

System Modeling and Simulation

• Modeling dynamic systems using Simulink.
• Comparing physics-based and data-driven modeling approaches.
• System visualization with Unity.

Real-Time Data Integration

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

AI and Machine Learning in Digital Twins

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

Visualization and Dashboards

• Designing interfaces for monitoring twin performance.
• Exploring 3D and 2D visualization options.
• 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 pipelines.
• Deployment and testing within a simulated environment.

Maintaining and Scaling Digital Twins

• Lifecycle management and model updates.
• Ensuring interoperability and adhering to standards.
• Scaling solutions to multiple assets or processes.

Summary and Future Directions