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.