Predictive Digital Twins: Foundations and Applications addresses the theoretical foundations, practical applications, and emerging trends associated with predictive digital twins. Specifically focusing on predictive capabilities, digital twins designated for this purpose are commonly known as predictive digital twins. Despite the growing recognition of their importance, discussions surrounding predictive digital twins remain fragmented and lacking a comprehensive resource. This gap becomes particularly pronounced in the academic world, where, as a university professor teaching a master's program course in digital twins, there arises a pressing need for a dedicated reference book to furnish students with a structured and in-depth exploration of predictive digital twins.
The book fills the existing void in literature and academia, providing students, researchers, and practitioners with a valuable resource to enhance their understanding of this cutting-edge concept. The digital twin concept stands as a pivotal facilitator in the ongoing Industry 4.0 revolution, with one of its most significant advantages lying in its capacity to offer precise predictions.
The book fills the existing void in literature and academia, providing students, researchers, and practitioners with a valuable resource to enhance their understanding of this cutting-edge concept. The digital twin concept stands as a pivotal facilitator in the ongoing Industry 4.0 revolution, with one of its most significant advantages lying in its capacity to offer precise predictions.
Table of Contents
1. Introduction to digital twins2. Fundamental aspects of predictive digital twins
3. Modelling and simulation of dynamic systems
4. State and parameter estimation
5. Sensor and actuator fault diagnosis
6. Data-driven discovery of governing equations
7. Prediction methods for digital twins
8. Model-based predictive digital twins
9. Data-driven predictive digital twins
10. Case study I: predictive digital twins for autonomous marine vessels
11. Case study II: predictive digital twins for unmanned aerial vehicles
12. Case study III: predictive digital twins for wind energy applications
13. Case study IV: predictive digital twins for healthcare applications
14. Future of predictive digital twins
Authors
Agus Hasan Norwegian University of Science and Technology (NTNU), Department of ICT and Natural Sciences, �lesund, Norway.Agus Hasan is a professor in cyber-physical systems at department of ICT and natural sciences, Norwegian University of Science and Technology (NTNU). He received his PhD in cybernetics from department of cybernetics engineering, NTNU and BSc in mathematics from department of mathematics, Bandung Institute of Technology. His research interests are in the areas of system dynamics, digital twins, and autonomous systems. He is IEEE senior member and serves as IEEE technical committee member on aerial robotics and unmanned aerial vehicles and IFAC technical committee member on distributed parameter systems. He is a recipient of ASME Best Paper Award in Mechatronics in 2015.
