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

A Gentle Introduction to Data, Learning, and Model Order Reduction Francisco Chinesta Primary Author Angelo Pasquale Primary Author Daniele Di Lorenzo Primary Author Icíar Alfaro Primary Author David González Primary Author Nicolas Hascoët Primary Author Amine Ammar Primary Author Chady Ghnatios Primary Author Victor Champaney Primary Author Elías Cueto Primary Author Dominique Baillargeat Primary Author mixed material bibliography Cham Springer Nature 2025 en English 227 Halaman 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 NONE 9783031875724 Perpustakaan UMA Online Public Access Catalog (OPAC) A Gentle Introduction to Data, Learning, and Model Order Reduction 978-3-031-87572-4.jpg.jpg 23660 2026-08-18 15:07:03 2026-08-18 15:07:03 machine generated