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

Fundamentals of Digital Twins

• Core concepts and the evolution of digital twin technology
• Key applications across manufacturing, energy, and supply chain sectors
• Architectural frameworks and the digital twin lifecycle

System Simulation and Modeling

• Simulating dynamic systems using Simulink
• Comparing physics-based versus data-driven modeling approaches
• Rendering system visuals with Unity

Real-Time Data Connectivity

• Establishing connectivity via MQTT and OPC-UA protocols
• Managing data streams using Node-RED
• Capturing sensor and machine telemetry for the twin

AI and ML Integration in Digital Twins

• Embedding AI models for predictive analytics and process optimization
• Deploying TensorFlow or PyTorch on live data streams
• Training models based on simulation outputs

Visualization and Dashboard Design

• Crafting user interfaces for effective twin monitoring
• Exploring 2D and 3D visualization capabilities
• Building custom dashboards with live insights

Practical Case Study: Creating a Digital Twin Prototype

• End-to-end architecture of a manufacturing asset twin
• Setting up data pipelines and machine learning components
• Deployment and validation in a simulated setting

Scaling and Maintenance of Digital Twins

• Managing the lifecycle and ongoing updates
• Ensuring interoperability and adherence to industry standards
• Expanding twin solutions across multiple assets or processes

Conclusion and Future Roadmap