Digital Twins: Core Principles and AI Integration offers a structured and up-to-date overview of digital twin technology, combining foundational principles with the rapidly growing role of artificial intelligence (AI). This book introduces the core concepts, modeling approaches, and software and systems engineering foundations needed to design and implement digital twins effectively. It then explores architectural methods, lifecycle management, interoperability, and the alignment between physical systems and their digital representations. A central part of this book focuses on data science and AI-enabled digital twins, demonstrating how machine learning, deep learning, generative AI, and autonomous agents enhance predictive analytics, optimization, anomaly detection, and automated decision-making. Integration with Internet of Things (IoT), cloud-edge infrastructures, big data analytics, and XR technologies further shows how intelligent digital twins evolve into adaptive and interactive systems. Real-world applications from manufacturing, agriculture, food systems, energy, mobility, healthcare, and urban environments illustrate the practical value of AI-driven digital twins. This book concludes with key challenges and future directions, including trustworthy AI, security, data governance, and the scaling of digital twin ecosystems.
