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

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