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

Foundations of Digital Twins

• Core concepts and the historical evolution of digital twins.
• Real-world applications in manufacturing, energy, and supply chain logistics.
• Structural architecture and the operational lifecycle of digital twins.

Modeling Systems and Running Simulations

• Simulating dynamic systems using Simulink.
• Contrasting physics-based approaches with data-driven modeling techniques.
• Visualizing system behavior using Unity.

Integrating Live Data Streams

• Establishing connectivity through MQTT and OPC-UA protocols.
• Managing data streams with Node-RED.
• Ingesting data from sensors and machinery into the digital twin.

Applying AI and Machine Learning to Digital Twins

• Embedding AI models to enhance prediction and system optimization.
• Working with TensorFlow or PyTorch on live data feeds.
• Training models based on simulation results.

Visualization and Dashboard Creation

• Designing intuitive interfaces for monitoring twin performance.
• Exploring 3D and 2D visualization capabilities.
• Building custom dashboards that provide real-time insights.

Practical Application: Prototyping a Digital Twin

• Complete design process for a manufacturing asset twin.
• Setting up data integration and machine learning workflows.
• Deploying and testing the solution in a simulated context.

Managing and Scaling Digital Twin Environments

• Handling lifecycle management and necessary updates.
• Ensuring interoperability and adherence to industry standards.
• Expanding solutions to cover multiple assets or complex processes.

Conclusions and Future Directions