A Toolbox for Digital Twins
From Model-Based to Data-Driven
Published on 30. September 2022
Book
Paperback/Softback
832 pages
978-1-61197-696-0 (ISBN)
Description
A Toolbox for Digital Twins: From Model-Based to Data-Driven brings together the mathematical and numerical frameworks needed for developing digital twins (DTs). Starting from the basics-probability, statistics, numerical methods, optimization, and machine learning-and moving on to data assimilation, inverse problems, and Bayesian uncertainty quantification, the book provides a comprehensive toolbox for DTs.
Readers will find
guidelines and decision trees to help the reader choose the right tools for the job,
emphasis on the design process, denoted as the "inference cycle," whose aim is to propose a global methodology for complex problems,
a comprehensive reference section with all recent methods, covering both model-based and data-driven approaches, and
a vast selection of examples and all accompanying code.
A Toolbox for Digital Twins: From Model-Based to Data-Driven is for researchers and engineers, engineering students, and scientists in any domain where data and models need to be coupled to produce digital twins.
Readers will find
guidelines and decision trees to help the reader choose the right tools for the job,
emphasis on the design process, denoted as the "inference cycle," whose aim is to propose a global methodology for complex problems,
a comprehensive reference section with all recent methods, covering both model-based and data-driven approaches, and
a vast selection of examples and all accompanying code.
A Toolbox for Digital Twins: From Model-Based to Data-Driven is for researchers and engineers, engineering students, and scientists in any domain where data and models need to be coupled to produce digital twins.
More details
Series
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Weight
800 gr
ISBN-13
978-1-61197-696-0 (9781611976960)
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Schweitzer Classification
Person
Mark Asch is full professor of applied mathematics at Universite de Picardie Jules Verne. His research deals with data assimilation, inverse problems, and their coupling with machine learning methods. Recent research includes acoustic monitoring of endangered whale species and optimal design of greener Li-ion batteries. For more than 30 years, he has taught applied statistics, machine learning, data assimilation, and numerical analysis, as well as consulted for industry. He has occupied posts at the Ministry of Research and Innovation, the ANR, and the CNRS, and recently spent two years on secondment in a very large multinational.