A Gentle Introduction to Data, Learning, and Model Order Reduction : Techniques and Twinning Methodologies / Francisco Chinesta, Angelo Pasquale, Elías Cueto, Victor Champaney, Chady Ghnatios, Amine Ammar, Nicolas Hascoët, David González, Icíar Alfaro, and Daniele Di Lorenzo.

Abstract -- Extended summary -- Part 1.Around Data -- Part 2.Around Learning -- Part 3. Around Reduction -- Part 4. Around Data Assimilation & Twinning.

This open access book explores the latest advancements in simulation performance, driven by model order reduction, informed and augmented machine learning technologies and their combination into the so-called hybrid digital twins. It provides a comprehensive review of three key frameworks shaping modern engineering simulations: physics-based models, data-driven approaches, and hybrid techniques that integrate both. The book examines the limitations of traditional models, the role of data acquisition in uncovering underlying patterns, and how physics-informed and augmented learning techniques contribute to the development of digital twins. Organized into four sections--Around Data, Around Learning, Around Reduction, and Around Data Assimilation & Twinning--this book offers an essential resource for researchers, engineers, and students seeking to understand and apply cutting-edge simulation methodologies

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2025. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.