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A Gentle Introduction to Data, Learning, and Model Order Reduction : Techniques and Twinning Methodologies / by Francisco Chinesta, Elías Cueto, Victor Champaney, Chady Ghnatios, Amine Ammar, Nicolas Hascoët, David González, Icíar Alfaro, Daniele Di Lorenzo, Angelo Pasquale, Dominique Baillargeat.
Springer Nature - Springer Nature Link Journals and eBooks - Fully Open Access Available online
View onlineSpringer eBooks EBA - Intelligent Technologies and Robotics Collection 2025 Available online
View online- Format:
- Book
- Author/Creator:
- Chinesta, Francisco, Author.
- Cueto, Elías, Author.
- Champaney, Victor., Author.
- Ghnatios, Chady., Author.
- Ammar, Amine., Author.
- Hascoët, Nicolas., Author.
- González, David, Author.
- Alfaro, Icíar., Author.
- Di Lorenzo, Daniele., Author.
- Pasquale, Angelo., Author.
- Baillargeat, Dominique., Author.
- Series:
- Studies in Big Data, 2197-6511 ; 174
- Language:
- English
- Subjects (All):
- Computational intelligence.
- Mathematics--Data processing.
- Mathematics.
- Machine learning.
- Computational Intelligence.
- Computational Science and Engineering.
- Machine Learning.
- Local Subjects:
- Computational Intelligence.
- Computational Science and Engineering.
- Machine Learning.
- Physical Description:
- 1 online resource (XVI, 227 p. 33 illus., 29 illus. in color.)
- Edition:
- 1st ed. 2025.
- Place of Publication:
- Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
- 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.
- Contents:
- Abstract
- Extended summary
- Part 1.Around Data
- Part 2.Around Learning
- Part 3. Around Reduction
- Part 4. Around Data Assimilation & Twinning.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 3-031-87572-9
- OCLC:
- 1535964046
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