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.
• Architecture and lifecycle of digital twin systems.

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

• Leveraging MQTT and OPC-UA for connectivity.
• Streaming data with Node-RED.
• Ingesting sensor and machine data into the twin.

AI and Machine Learning in Digital Twins

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

Visualization and Dashboards

• Designing user interfaces for twin monitoring.
• 3D and 2D visualization options.
• Creating custom dashboards with real-time insights.

Case Study: Building a Digital Twin Prototype

• End-to-end design of a manufacturing asset twin.
• Setting up data integration and machine learning.
• Deployment and testing in a simulated environment.

Maintaining and Scaling Digital Twins

• Lifecycle management and ongoing updates.
• Interoperability and industry standards.
• Scaling to multiple assets or processes.

Summary and Next Steps