A Gentle Introduction to Data, Learning, and Model Order Reduction [electronic resource] : 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.

За: Інтелектуальна відповідальність: Вид матеріалу: Текст Серія: Studies in Big Data ; 174Публікація: Cham : Springer Nature Switzerland : Imprint: Springer, 2025Видання: 1st ed. 2025Опис: XVI, 227 p. 33 illus., 29 illus. in color. online resourceТип вмісту:
  • text
Тип засобу:
  • computer
Тип носія:
  • online resource
ISBN:
  • 9783031875724
Тематика(и): Додаткові фізичні формати: Printed edition:: Немає назви; Printed edition:: Немає назви; Printed edition:: Немає назвиДесяткова класифікація Дьюї:
  • 006.3 23
Класифікація Бібліотеки Конгресу:
  • Q342
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Вміст:
Abstract -- Extended summary -- Part 1.Around Data -- Part 2.Around Learning -- Part 3. Around Reduction -- Part 4. Around Data Assimilation & Twinning.
У: Springer Nature eBookЗведення: 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.
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Abstract -- Extended summary -- Part 1.Around Data -- Part 2.Around Learning -- Part 3. Around Reduction -- Part 4. Around Data Assimilation & Twinning.

Open Access

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.

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