A Toolbox for Digital Twins: From Model-Based to Data-Driven - Math in Industry

Brings together the mathematical and numerical frameworks needed for developing digital twins (DTs). Starting from the basics - probability, statistics, numerical methods, optimization, machine learning - and moving on to data assimilation, inverse problems, and Bayesian uncertainty quantification, the book provides a comprehensive toolbox for DTs.