Digital Twins Core Principles and AI Integration By Bedir Tekinerdogan, Cor Verdouw
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- 396
- File size
- 10.43 MB
- Format
- Digital PDF
- Course
- Education
- Category
- eBook[PDF]
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About this ebook
The Digital Twins: Core Principles and AI Integration by Bedir Tekinerdogan and Cor Verdouw covers a broad range of topics detailing the structure, architecture, intelligence layer, and practical applications of digital twin ecosystems. [1, 2]
The text is organized into a clear progression that spans five core areas: [1]
Foundations & Systems Engineering
- Core Concepts: Fundamental definitions, modeling approaches, and the underlying mechanics of bridging physical entities with virtual replicas. [1, 2]
- Engineering Principles: The software and systems engineering foundations needed to effectively design, scale, and build digital twin infrastructure. [1, 2]
Architecture & System Lifecycle
- Architectural Methods: Frameworks for data integration, synchronization, and ensuring alignment between physical systems and their digital twins. [1, 2]
- Lifecycle Management: Managing a digital twin from its initial design phase through operational updates and decommissioning. [1]
- Interoperability: Protocols and strategies for enabling communication between disparate data environments, platforms, and external software ecosystems. [1, 2]
AI & Data Science Integration
- Advanced AI Technologies: The direct role of machine learning, deep learning, generative AI, and autonomous agents in driving system automation.
- Intelligent Capabilities: How AI transforms static models into dynamic systems capable of predictive analytics, optimization, anomaly detection, and automated decision-making.
- Complementary Tech Stacks: Integration frameworks linking digital twins with the Internet of Things (IoT), cloud-edge infrastructures, Big Data analytics, and Extended Reality (XR). [1, 2, 3]
Real-World Applications & Industry Verticals
Case studies and operational deployment scenarios across several major industrial sectors: [1]
- Manufacturing and automated production lines.
- Agriculture and complex food production systems.
- Energy grid management and resource mobility.
- Healthcare optimization and patient monitoring systems.
- Urban environments and smart city infrastructures. [1, 2]
Governance, Challenges & Future Directions
- Trustworthy AI: Operational safety, transparency, and ethical considerations surrounding autonomous digital twins.
- Data Security & Governance: Resolving complex issues related to cybersecurity vulnerabilities, data ownership, and strict privacy regulations.
- Scaling Ecosystems: The technological runway required to scale isolated digital twins into interconnected, macro-level digital twin networks
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