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

Foundamentals of Digital Twins

• Core concepts and the evolution of digital twin technology
• Practical applications in manufacturing, energy, and logistics sectors
• Overview of digital twin architecture and lifecycle management

System Modeling and Simulation

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

Real-Time Data Integration

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

AI and Machine Learning in Digital Twins

• Incorporating AI models for prediction and optimization tasks
• Leveraging TensorFlow or PyTorch with live data feeds
• Training models based on simulation outputs

Visualization and Dashboard Design

• Creating user interfaces for effective twin monitoring
• Exploring 3D and 2D visualization capabilities
• Building custom dashboards with 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
• Deploying and testing within a simulated environment

Maintenance and Scaling of Digital Twins

• Managing the lifecycle and applying updates
• Ensuring interoperability and adhering to standards
• Scaling solutions to accommodate multiple assets or processes

Recap and Future Directions