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A Gentle Introduction to Data, Learning, and Model Order Reduction

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 mo...

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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
Formato: Online
Idioma:inglés
Publicado: Springer Nature 2025
Subjects:
Data reduction
Machine learning
Model Order Reduction
Digital Twins
Hybrid modelling
thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence
thema EDItEUR::P Mathematics and Science::PB Mathematics::PBK Calculus and mathematical analysis::PBKS Numerical analysis
thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
Acceso en liña:ONIX_20250813T121456_9783031875724_10
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Summary: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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