Studies in Big Data: Techniques and Twinning Methodologies

Chinesta, Francisco; Cueto, Elías; Champaney, Victor; Ghnatios, Chady; Ammar, Amine; Hascoët, Nicolas; González, David; Alfaro, Icíar; Di Lorenzo, Daniele; Pasquale, Angelo; Baillargeat, Dominique

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

Details

Published by: Springer

Publication Date: 2025-07-23

Format: Hardcover

ISBN-13: 9783031875717

DOI: 10.1007/978-3-031-87572-4

Dimensions: 235cm x155cm

Pages: 227