A Gentle Introduction to Data, Learning, and Model Order Reduction: Techniques and Twinning Methodologies (Studies in Big Data, 174) - Softcover

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

Softcover

ISBN 10: 3031875745 ISBN 13: 9783031875748

Publisher: Springer, 2026

Synopsis

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

About this title

About the Author

Francisco Chinesta – Professor of Computational Physics at Arts et Métiers Institute of Technology, Paris and programme director at CNRS@CREATE, Singapore. His research focuses on computational physics, model order reduction, and hybrid artificial intelligence.

Elias Cueto – Professor of Continuum Mechanics at Universidad de Zaragoza. His research covers model order reduction, artificial intelligence and computational mechanics.

Victor Champaney – Researcher at Arts et Métiers Institute of Technology, Paris. His work specializes in model order reduction, hybrid modeling and frugal AI techniques.

Chady Ghnatios – Professor of Mechanical Engineering at University of North Florida, USA. His research focuses on model order reduction, advanced simulation, machine learning and hybrid modeling.

Amine Ammar – Professor of Computational Mechanics at Arts et Métiers Institute of Technology, Angers. His expertise lies in kinetic theory models, model reduction, and computational material forming.

Nicolas Hascoët – Associate Professor at Arts et Métiers Institute of Technology, Paris. His research focuses on machine learning and data science for industrial applications.

David Gonzalez – Professor of Continuum Mechanics at Universidad de Zaragoza. His research interests include model reduction, real-time computational simulations, and physics-informed AI.

Icíar Alfaro – Associate Professor at Universidad de Zaragoza. She specializes in numerical methods, solid mechanics, and physics-informed neural networks.

Daniele Di Lorenzo – Researcher at Arts et Métiers Institute of Technology, Paris. His research focuses on inverse analysis, hybrid modeling, and digital twins for structural health monitoring.

Angelo Pasquale – Researcher in Computational Mechanics at Arts et Métiers Institute of Technology, Paris. He specializes in AI-enhanced simulations, model order reduction and multiscale modeling.

Dominique Baillargeat – Professor at the University of Limoges and Director of CNRS@CREATE at Singapore. His research focuses on high-frequency electronics, nanotechnologies, and advanced modeling and simulation techniques using Hybrid-AI.

From the Back Cover

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

Edition details

• Publisher: Springer
• Publication date: 2026
• Language: English
• ISBN 10: 3031875745
• ISBN 13: 9783031875748
• Binding: Paperback
• Number of pages: 243