Predictive Digital Twins
Foundations and Applications
- 1st Edition - December 1, 2026
- Latest edition
- Author: Agus Hasan
- Language: English
Predictive Digital Twins: Foundations and Applications addresses the theoretical foundations, practical applications, and emerging trends associated with predictive digital twins.… Read more
Description
Description
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.
Key features
Key features
- Provides the clear explanations and theories underpinning predictive digital twins to facilitate effective teaching and research
- Includes real-world applications and success stories that illustrate the practical implementation of predictive digital twins across different industries
- Provides practical insights into how to implement predictive digital twins for improving processes, especially in predictive maintenance
- Gives guidance on best practices for developing, managing, and optimizing predictive digital twin systems
Readership
Readership
Academia: Professors and Instructors: Engaged in teaching courses related to digital twins, simulation & visualization, or relevant disciplines at the master's level; Researchers: Scholars exploring advancements in predictive digital twins, seeking theoretical foundations, and contributing to academic discourse
Table of contents
Table of contents
1. Introduction to digital twins
1.1 Definition, history, and typology
1.2 Digital twins in the context of industry 4.0
2. Fundamental aspects of predictive digital twins
2.1 Key components and characteristics
2.2 The role of predictive digital twins
2.3 Challenges and opportunities
3. Modelling and simulation of dynamic systems
3.1 Principle of dynamic systems modelling
3.2 Discretization and simulation techniques
3.3 Applications in digital twins
3.4 Exercise
4. State and parameter estimation
4.1 State and parameter estimation problems
4.2 Deterministic approach using adaptive observer
4.3 Stochastic approach using adaptive Kalman filter
4.4 Exercise
5. Sensor and actuator fault diagnosis
5.1 Fault diagnosis problems
5.2 Actuator fault diagnosis
5.3 Sensor fault diagnosis
5.4 Exercise
6. Data-driven discovery of governing equations
6.1 Inverse problem
6.2 Methodology
6.3 Exercise
7. Prediction methods for digital twins
7.1 Predictions methods in digital twins
7.2 Exercise
8. Model-based predictive digital twins
8.1 Model-based predictions
8.2 Exercise
9. Data-driven predictive digital twins
9.1 Data-driven predictions
9.2 Exercise
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
1.1 Definition, history, and typology
1.2 Digital twins in the context of industry 4.0
2. Fundamental aspects of predictive digital twins
2.1 Key components and characteristics
2.2 The role of predictive digital twins
2.3 Challenges and opportunities
3. Modelling and simulation of dynamic systems
3.1 Principle of dynamic systems modelling
3.2 Discretization and simulation techniques
3.3 Applications in digital twins
3.4 Exercise
4. State and parameter estimation
4.1 State and parameter estimation problems
4.2 Deterministic approach using adaptive observer
4.3 Stochastic approach using adaptive Kalman filter
4.4 Exercise
5. Sensor and actuator fault diagnosis
5.1 Fault diagnosis problems
5.2 Actuator fault diagnosis
5.3 Sensor fault diagnosis
5.4 Exercise
6. Data-driven discovery of governing equations
6.1 Inverse problem
6.2 Methodology
6.3 Exercise
7. Prediction methods for digital twins
7.1 Predictions methods in digital twins
7.2 Exercise
8. Model-based predictive digital twins
8.1 Model-based predictions
8.2 Exercise
9. Data-driven predictive digital twins
9.1 Data-driven predictions
9.2 Exercise
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
Product details
Product details
- Edition: 1
- Latest edition
- Published: December 1, 2026
- Language: English
About the author
About the author
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Agus Hasan
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.
Affiliations and expertise
Norwegian University of Science and Technology (NTNU), Department of ICT and Natural Sciences, Ålesund, Norway