The publication of A Gentle Introduction to Data, Learning, and Model Order Reduction: Techniques and Twinning Methodologies marks an important milestone for the growing field of hybrid digital twins. Published by Springer Nature as an Open Access volume, the book brings together internationally recognized researchers to present a unified introduction to the scientific foundations behind next-generation engineering simulation.  

Co-authored by Francisco Chinesta, Victor Champaney, Daniele Di Lorenzo, Angelo Pasquale, Dominique Baillargeat and several research collaborators, the book reflects years of work carried out across academia and industry, including research that contributed to the creation of Duoverse and its AI-enhanced digital twin platform.  

Rather than treating physics-based modelling and artificial intelligence as competing approaches, the authors demonstrate how they can complement one another. The book introduces the concepts of scientific machine learning, model order reduction, data assimilation, and hybrid digital twins, showing how advanced numerical simulations can be accelerated while preserving physical consistency and engineering reliability.  

Organized as a progressive learning resource, the book covers the complete workflow from data processing and machine learning techniques to reduced-order modelling and digital twinning methodologies. With more than 30 chapters and practical examples, it provides researchers, engineers and graduate students with both the theoretical foundations and practical tools needed to develop next-generation simulation technologies.  

Making the book freely available reflects the authors' commitment to democratizing access to advanced engineering knowledge. As hybrid AI continues to transform industries ranging from manufacturing and mobility to energy and smart cities, these methodologies are becoming essential for building digital twins that are faster, more adaptive and capable of supporting real-time decision-making. 

For Duoverse, this publication represents both the scientific foundations of its technology and its continued commitment to advancing the state of the art through collaboration between research and industry.