Elsevier Digital Twins: Core Principles and AI Integration

Digital Twins: Core Principles, System Engineering, and AI Integration provides a comprehensive overview of digital twin technology, a cutting-edge innovation that bridges the physical and digital worlds. The book addresses common challenges such as data integration, security, scalability, and the alignment of digital twin models with actual physical processes. After presenting core concepts of digital twins for software engineering, the book discusses integration with advanced digital solutions such as AI, IoT, Cloud computing, Big Data Analytics, and Extended Reality (XR). Next, the authors provide readers with a thorough presentation of digital twins' applications in a variety of settings and industry/research topics. Finally, the book concludes with a discussion of challenges and solutions, along with future trends in digital twins research and development. As digital twin technology evolves, its integration with various advanced digital solutions is becoming essential for achieving real-time insights and autonomous decision-making. Challenges include understanding the interoperability of these technologies, managing data complexity, ensuring security, and optimizing for low-latency environments. Covers the intersection of digital twin technology with a range of advanced digital solutions Presents a valuable resource for those seeking to understand and implement these integrations Provides real-world examples from various industries, such as smart manufacturing, healthcare, smart cities, agriculture, and food Illustrates successful integration strategies and outcomes Explores future trends and research opportunities, providing insights into how digital twins and advanced digital solutions will evolve and impact various sectors