Publisher's Synopsis
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.
