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Course Outline
Introduction to Digital Twins for Government
- Overview of digital twin concepts and their evolution
- Applications in manufacturing, energy, and logistics sectors
- Architecture and lifecycle management of digital twins
System Modeling and Simulation for Government
- Dynamic system modeling using Simulink
- Comparison between physics-based and data-driven modeling approaches
- Visualization of systems with Unity
Real-Time Data Integration for Government
- Utilizing MQTT and OPC-UA for connectivity
- Streaming data using Node-RED
- Incorporating sensor and machine data into digital twins
AI and Machine Learning in Digital Twins for Government
- Integration of AI models for predictive and optimization purposes
- Utilizing TensorFlow or PyTorch with live data streams
- Training machine learning models using simulation outputs
Visualization and Dashboards for Government
- Designing user interfaces for monitoring digital twins
- Options for 3D and 2D visualization
- Creating custom dashboards with real-time insights
Case Study: Building a Digital Twin Prototype for Government
- Comprehensive design of a manufacturing asset twin
- Data integration and machine learning setup
- Deployment and testing in a simulated environment
Maintaining and Scaling Digital Twins for Government
- Lifecycle management and system updates
- Ensuring interoperability and adherence to standards
- Scaling solutions to multiple assets or processes
Summary and Next Steps for Government
Requirements
- A foundational knowledge of system modeling or industrial operations for government
- Experience with Python or comparable programming languages
- Familiarity with data integration principles
Audience
- Digital transformation leaders for government
- Plant IT personnel for government
- Data architects for government
21 Hours