A Gentle Introduction to Data, Learning, and Model Order Reduction Techniques and Twinning Methodologies

• Main Authors: Chinesta, Francisco, Cueto, Elías, Champaney, Victor, Ghnatios, Chady, Ammar, Amine, Hascoët, Nicolas, González, David, Alfaro, Icíar, Di Lorenzo, Daniele, Pasquale, Angelo, Baillargeat, Dominique
• Format: eBook
• Language: English
• Published: Cham Springer Nature 2025
• Series: Studies in Big Data
• Subjects: Artificial intelligence; Calculus and mathematical analysis; Computer science; Computing and Information Technology; Data reduction; Digital Twins; Hybrid modelling; Machine learning; Mathematics; Mathematics and Science; Model Order Reduction; Numerical analysis
• ISBN: 3031875729; 9783031875724; 3031875710; 9783031875717
• DOI: 10.1007/978-3-031-87572-4

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