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Full Description
Enables readers to understand the concept of digital twins from an engineering perspective
Digital Twin technology is revolutionizing how we represent and interact with physical systems in the digital realm. Guided by a central question�can we derive valuable insights and system behavior from observational data?� The Foundation for Digital Twins bridges data-driven and model-based approaches to reveal how Digital Twin solutions can truly reflect the complexity of real-world environments and be effective tools to control how systems behave.
From modular software design and real-time data analysis to the growing role of artificial intelligence, this book shows us how to build scalable, adaptive Digital Twin architectures. Combining theory with practical strategies and use cases, it�s essential reading for researchers, developers, and practitioners shaping the next generation of digital systems.
Contents
Contents
Contributors
Foreword
Preface
Acknowledgments
Acronyms
Introduction
1 What is a Digital Twin ?
1.1 Introductory Concepts
1.1.1 Basic Definitions
1.1.2 Formalized Definitions
1.2 Digital Twin: a Rationale
1.2.1 Models and Modeling
1.2.2 DT as a Combination of Models
1.3 Digital Twin Definition: a step further
1.3.1 Digital Twin Properties
1.4 Digital Twin Representation, Model, and more
1.5 The Software Part of a Digital Twin
1.6 Specification Methodologies
1.7 Domain Knowledge and Operation in Digital Twins
1.8 Clarifying Scope and Terminology for Digital Twin Architectures
1.8.1 Digital Twin Architecture and Scope Definition
1.8.2 Evolving Terminology in Digital Twin Research
2 Models and Modeling Aspects of a Digital Twin
2.1 The Representational Aspects of a Digital Twin
2.2 Data Modeling
2.2.1 Data and Behavior Modeling
2.2.2 Data Models as Enablers of Passive Digital Twins
2.3 Behavior Modeling
2.4 Predictive Models
2.5 Prognosis Model
2.6 Prescriptive Model
2.7 A flexible Approach to multi-facet Models
2.7.1 Relationships between different Models
2.7.2 Models and Design, the Complexity of DT Modeling
2.7.3 Modeling the Environment
2.7.4 Context and Situation
2.7.5 From Top or Bottom ?
3 Foundational Data for a Digital Twin
3.1 Descriptive Models
3.2 Data-driven Architecture
3.3 Which Data to include in a Digital Twin
3.4 Organizing Data into Data Models
3.4.1 Minimal DT Structure and multiple types of Data
3.4.2 Data Representations for the Digital Twin
3.4.3 Data Models and Application Programming Interfaces
3.5 Data Models for a Digital Twin
3.5.1 Data Modeling Techniques and Semantic Enrichment
in Digital Twins
3.5.2 Existing Data Models for Digital Twins
3.6 Extending Data Models to Fit the Stakeholders' Needs
3.7 Relationships between Descriptive and Behavior Models
3.7.1 Data Exploitation Chain: from Passive to Prescriptive
DT
4 Digital Twin as a Behavior Model of a Physical System
4.1 Behavior Modeling
4.1.1 An Example: the Traffic Light System Behavior
4.1.2 Behavior Modeling Challenges and Needs
4.2 Foundational Behavior Models
4.3 Developing a Behavior Model
4.3.1 Implementing the Twin as a State Management
Component
4.3.2 Environment and DT Structure
4.4 AI and Digital Twins
4.4.1 Iterative Refinement of Digital Twin Behavior Models
using Generative AI
4.4.2 Extraction of Behavior Rules from Data
4.4.3 Enhancing AI Explainability in Digital Twins through
Behavior Models
4.4.4 Autonomic Digital Twins and Agentic AI
4.5 Simulation Models, Behavior Validation, and GenAI Integration
in Digital Twins
4.5.1 Applicable Simulation Models
4.5.2 MetaModel for Unified Execution and Simulation
4.5.3 Evolving the Traffic Light Digital Twin with Situation-
Aware Behavior Modeling
4.6 Behavior Models for Prognosis and Prescriptive Digital Twins
4.6.1 Behavior Models for Prognostic Digital Twins
4.6.2 Prescriptive Model and Integration with Prognosis
Capabilities
4.6.3 Recommendation Systems in Digital Twins
4.6.4 The Role of Autonomics in Prescriptive Digital Twins
4.6.5 Benefits of Behavior Models in Prescriptive Digital
Twins
4.7 Behavior Modeling Recap
5 Architecting and Implementing Digital Twin Systems: Approaches,
Guidelines and Best Practices
5.1 Requirements, Structural Concepts and Terminology for a Digital
Twin Architecture
5.1.1 Requirements and Properties of Digital Twins
5.1.2 State of the Art in Digital Twin Architectures
5.1.3 Reimagining a Versatile Digital Twin Architecture
5.2 Software Interaction Paradigms for Digital Twin Implementation
5.2.1 Advantages for DT Implementation
5.2.2 Home Automation Example
5.3 Componentization of DT Architecture
5.3.1 System Engine
5.3.2 Data Management
5.3.3 DT Engine
5.3.4 DT Life-cycle Management
5.4 Model First
5.4.1 Top-Down Digital Twins
5.4.2 Bottom-Up Digital Twins
5.4.3 Hybrid Approaches
5.4.4 Interacting DTs
5.5 From Modules to Components and Microservices: A Digital
Twin Perspective
5.5.1 Rationale for further Decomposition: Digital Twin
Specifics
5.5.2 Microservice Design Representation
5.5.3 Rationale for Microservice Decomposition: Digital
Twin Advantages
5.5.4 Preparing the Components for Deployment
5.5.5 Comparison of the Architecture with ISO 23247 Standard
5.6 Testing and Validation of Digital Twins
5.6.1 Importance of Testing and Validation
5.6.2 Continuous Impact within the Enterprise
5.6.3 Techniques and Approaches for Testing and Validation
5.6.4 Value of a Validated Digital Twin
6 Deploying and Operating a Digital Twin
6.1 Distribution of DT Components and Functions
6.1.1 Centralized or Distributed DT
6.1.2 A Deployment Scenario
6.2 Operating the Digital Twin
6.2.1 Product and Digital Twin Life-cycle Management
and Phase Transitions
6.2.2 Management of Digital Twin Functionalities
6.2.3 Artificial Intelligence for DT Management and Operation
6.2.4 Data Management for the Digital Twin
6.2.5 Management of the System Infrastructure
6.2.6 Additional relevant Topics
6.3 Example: Traffic Light Service Digital Twin
6.4 Digital Twin Impact on Organization Processes
6.5 Life-cycle Insights from Industrial Experiences
6.5.1 Life-cycle Phases
6.5.2 Exemplary Industrial Digital Twin Projects
6.5.3 Enablers and Barriers to Industrial Exploitation
6.5.4 Advantages and Enterprise Effort
7 Some Examples of Applicability of the Digital Twin Architecture
7.1 Introduction
7.2 Developing Digital Twins for Smart Cities: A Bottom-Up Approach
7.2.1 From Simple Digital Twins to Specialized Behavior
2.2 Microservices and Component Flexibility
7.2.3 Integrating Heterogeneous Data Streams for Holistic
Urban Insights
7.3 Network Digital Twin: the Edge-Cloud Continuum Representation
7.3.1 A Top-Down Approach for NDT Design
7.3.2 NDT Template: Monitoring and Optimization
7.3.3 Stakeholder Views and Insights
7.3.4 NDT Architecture Overview
7.3.5 Optimization and Prognosis: Example Workflows
7.4 The Challenge of DTs for Cultural Heritage
7.4.1 A Hybrid Approach: Bottom-Up and Top-Down
7.4.2 Multi-View Digital Twins for Artifacts
7.4.3 Web of related Digital Twins
7.4.4 Personalized and Adaptive Experiences
7.4.5 Case Study: Egyptian Scarabs and the Power of Digital
Twins
7.4.6 Context DT Architecture Support for Cultural Heritage
7.5 Digital Twin as an Integral Part of the Metaverse
7.5.1 State of the Art of Metaverse Platforms and Mapping
to the Context Digital Architecture
7.5.2 Integration Points and Mutual Enhancement
7.5.3 Use Cases: Education, Tourism, and Factory Management
7.6 Implementing Services with the Envisaged Architecture
8 The Digital Twin of the Future
8.1 The Evolution of DT
8.2 Evaluating Digital Twins as a General Solution
8.3 Promising Application Domains
8.4 Anticipated Evolution of Digital Twin Technologies
8.4.1 DT Platforms
8.4.2 DT Creation and Development
8.4.3 Decomposition of Modules
8.4.4 Extensible Architecture
8.4.5 Integrated Methodologies
8.4.6 AI Integration
8.4.7 Testing and Assessment
8.5 Improved Operations
8.5.1 Life-cycle Management
8.5.2 Managing the Switch of States
8.5.3 Operations Tools
8.6 Interoperability and Standards
8.6.1 Standardization Efforts
8.6.2 Open APIs and Data Models
8.7 Ethical, Privacy, and Trust Considerations in Digital Twin Systems
8.8 Human-in-the-Loop and User Experience
8.8.1 User-Centric Design
8.8.2 Visualization and Immersive Interfaces
8.9 Sustainability and Societal Impact
8.9.1 DTs for Sustainable Development
8.9.2 Societal Impact and Digital Inclusion
8.10 Future Steps
A Listings and Details
A.1 Descriptive Modeling
A.1.1 Data Model and Application Programming Interfaces
A.1.2 Extending the Data Model to Fit Stakeholder Needs
A.2 Behavior Modeling
A.2.1 NGSI-LD Data Model for Traffic Light Behavior
