liveBook · Manning

What you will learn in Digital Twins in Action

by Greg Biegel

Chapter by chapter

Chapter 1: Bridging the physical and digital worlds

1.1 What is a digital twin? · 1.2 Technologies enabling digital twins · 1.2.1 Internet of things · 1.2.2 Cloud computing · 1.2.3 Machine learning and artificial intelligence · 1.2.4 Visualization technologies and game engines · 1.2.5 Agentic AI and autonomous decision-making · 1.3 Digital twin capability levels · 1.3.1 Descriptive digital twin · 1.3.2 Informative digital twin · 1.3.3 Predictive digital twin · 1.3.4 Comprehensive digital twin · 1.3.5 Autonomous digital twin · 1.4 What are digital twins used for? · 1.4.1 Accelerating product development · 1.4.2 Reducing costs through predictive maintenance · 1.4.3 Optimizing performance and operational efficiency · 1.4.4 Training and simulation · 1.5 Digital twins across industries · 1.5.1 Mining, energy, and industrial · 1.5.2 Automotive · 1.5.3 Agriculture · 1.5.4 Defense · 1.5.5 Healthcare · 1.5.6 Infrastructure · 1.5.7 Smart buildings, cities, and states · 1.5.8 What a digital twin is not good for · 1.6 Building your first digital twin: Key considerations and challenges · 1.6.1 A clear definition of success · 1.6.2 Data quality · 1.6.3 Skills gap · 1.6.4 Build vs. buy · 1.6.5 Governance and security · 1.7 Summary

What you learn in chapter 1 →

Chapter 2: Mapping physical systems to digital representations

2.1 Defining your objectives · 2.1.1 Identify specific challenges and opportunities · 2.1.2 Establish measurable performance indicators and baselines · 2.1.3 Map decision-making improvements · 2.1.4 Define a minimum viable digital representation · 2.2 A home digital twin to optimize energy and water use · 2.2.1 Mapping the home to a digital representation · 2.2.2 Start with what you have · 2.3 Information sources for digital representation · 2.3.1 Historical records · 2.3.2 Photographs · 2.3.3 Videos · 2.3.4 Engineering documents · 2.3.5 External systems · 2.4 Spatial and geometric representations · 2.4.1 2D geometric models · 2.4.2 3D geometric models · 2.5 Spatial mapping and reference systems · 2.5.1 Coordinate systems and projections · 2.5.2 Coordinate transformations and conversions · 2.6 Deciding what you need · 2.7 Summary

What you learn in chapter 2 →

Chapter 3: Sensing the real world

3.1 How sensors work · 3.1.1 The sensor signal chain · 3.1.2 Key sensor characteristics · 3.2 Selecting the right sensors for your digital twin · 3.2.1 Defining what you’re measuring · 3.2.2 Matching sensor capabilities to real-world constraints · 3.2.3 Verifying integration before purchasing · 3.2.4 Choosing sensors for a home digital twin · 3.3 Connecting sensors: Communication technologies · 3.3.1 Short-range technologies (<100 m) · 3.3.2 Long-range technologies (>100 m) · 3.3.3 Choosing the right technology · 3.3.4 Universal data transport architecture · 3.4 Real-world data collection strategies · 3.4.1 Edge processing · 3.4.2 When electronic sensing isn’t enough · 3.4.3 Manual sensing strategies · 3.5 Data processing · 3.5.1 Understanding IoT data characteristics · 3.5.2 Message decoding · 3.5.3 Sensor fusion · 3.6 Scalability and device management · 3.7 Bringing it all together · 3.7.1 Data flow walkthrough · 3.7.2 Architectural principles · 3.8 Summary

What you learn in chapter 3 →

Chapter 4: Data integration and management

4.1 Types of data · 4.1.1 Reference data · 4.1.2 Time-series data · 4.1.3 Unstructured and semi-structured data · 4.1.4 Spatial data · 4.1.5 Derived data · 4.2 Data sources · 4.2.1 OT data sources · 4.2.2 IT data sources · 4.2.3 The convergence of IT and OT · 4.2.4 External data sources · 4.3 Data structures and storage · 4.3.1 Relational and transactional data · 4.3.2 Columnar and analytical storage · 4.3.3 Time-series data storage · 4.3.4 Semi-structured and document data · 4.3.5 Object storage, data lakes, and lakehouses · 4.3.6 Specialized storage systems · 4.3.7 Data life cycle management · 4.4 Data ingestion · 4.4.1 Batch data ingestion · 4.4.2 Streaming data ingestion · 4.4.3 API integration · 4.4.4 ETL and data transformation · 4.5 Data governance and compliance · 4.5.1 Data classification and sensitivity · 4.5.2 Regulatory compliance · 4.5.3 Access control and authentication · 4.5.4 Data quality · 4.6 Selecting the right combination · 4.7 Data store for a home digital twin · 4.7.1 Storing time-series data · 4.7.2 Storing object data · 4.7.3 Storing relationship data · 4.8 Summary

What you learn in chapter 4 →

Chapter 5: Modeling reality

5.1 Making sense of data · 5.1.1 From measurements to decisions · 5.1.2 The knowledge engineering challenge · 5.2 Understanding context in digital twins · 5.2.1 Types of context · 5.2.2 How to contextualize data · 5.2.3 The importance of context in generative AI · 5.3 Ontologies as a blueprint for understanding · 5.3.1 Core components of an ontology for a digital twin · 5.3.2 Defining ontologies for digital twins · 5.3.3 Choosing an ontology for your model · 5.4 Knowledge graphs · 5.4.1 Graph theory · 5.4.2 Building a knowledge graph as a labeled property graph · 5.4.3 Graph traversals · 5.4.4 Building a knowledge graph of my home · 5.4.5 Choosing a knowledge graph service · 5.5 Standards and interoperability · 5.5.1 A framework for implementing standards in your digital twin · 5.5.2 Practical standards assessment · 5.5.3 A practical assessment framework · 5.5.4 Real-world example: The Asset Administration Shell · 5.6 Serving the model of reality · 5.6.1 Context-aware data retrieval · 5.6.2 Building an API layer for my home digital twin · 5.6.3 The power of a GraphQL schema · 5.7 Summary

What you learn in chapter 5 →

Chapter 6: 2D Visualization and insight

6.1 Why graphical visualization matters in digital twins · 6.1.1 Data-to-visual mapping principles · 6.1.2 User experience considerations · 6.2 2D visualization techniques · 6.2.1 Charts, graphs, and data visualization · 6.2.2 Dashboards and control panels · 6.2.3 Schematic diagrams · 6.2.4 Spatial 2D visualization · 6.2.5 Heatmaps · 6.3 2D visualization performance considerations · 6.3.1 Choosing the right rendering technology · 6.3.2 Data decimation and aggregation for time-series data · 6.4 Building effective visualizations with Grafana · 6.4.1 Why Grafana works well for digital twins · 6.4.2 Choosing a visualization platform · 6.5 Summary

What you learn in chapter 6 →

Chapter 7: Spatial context and 3D insight

7.1 3D visualization fundamentals for digital twins · 7.1.1 More complex models · 7.1.2 Texturing the mesh · 7.1.3 Web-based 3D visualization · 7.2 Creating a mesh-based 3D geometric model · 7.2.1 Serving the model via the web · 7.2.2 Spatially locating the model · 7.3 Data integration · 7.3.1 The importance of spatially locating data in 3D · 7.3.2 Annotation and markup · 7.3.3 3D visualization performance considerations · 7.4 Capturing reality in 3D · 7.4.1 360-degree photography · 7.4.2 Photogrammetry · 7.4.3 Neural Radiance Fields · 7.4.4 Gaussian splatting · 7.4.5 Light detection and ranging · 7.4.6 Simultaneous localization and mapping · 7.4.7 Universal Scene Description and the NVIDIA Omniverse · 7.5 Extended reality · 7.5.1 Virtual reality: Full immersion · 7.5.2 Augmented reality: Contextual overlay · 7.5.3 Mixed reality: Blending worlds · 7.6 Building effective visualizations · 7.6.1 Security and access control · 7.6.2 Usability and user experience · 7.6.3 Data-to-visualization pipeline and scalability · 7.7 Summary

What you learn in chapter 7 →

Chapter 8: Integrating inference and intelligence

8.1 A practical data pipeline · 8.1.1 Cleaning and normalization · 8.1.2 Time alignment and resampling · 8.1.3 Feature extraction · 8.1.4 Storage · 8.2 Anomaly and fault detection · 8.2.1 Rule-based detection and its limitations · 8.2.2 Statistical approaches · 8.2.3 Unsupervised learning · 8.3 Predictive modeling and machine learning · 8.3.1 Training a forecasting model · 8.3.2 Applying AutoML · 8.3.3 Supervised learning · 8.4 Generative AI · 8.4.1 Retrieval-augmented generation · 8.4.2 Agentic AI · 8.4.3 Building an agent for the home digital twin · 8.4.4 When agents complement or replace control strategies · 8.4.5 Multi-agent systems · 8.5 Choosing the right approach · 8.5.1 Operational viability · 8.5.2 Safety and human in the loop · 8.5.3 Cost · 8.6 Summary

What you learn in chapter 8 →

Chapter 9: Predicting outcomes with simulation

9.1 Modeling and simulating physical systems · 9.1.1 The digital twin advantage · 9.1.2 From static models to dynamic simulation · 9.1.3 Continuous versus discrete event simulation · 9.2 Continuous systems · 9.2.1 Simulating a continuous system · 9.2.2 The initial value problem · 9.3 Acausal modeling for digital twins · 9.3.1 How it works · 9.3.2 Modeling tank drainage in OpenModelica · 9.3.3 Executing simulations · 9.3.4 Integrating sensor data with continuous simulation · 9.4 Simulating processes with discrete event simulation · 9.4.1 Why DES matters for digital twins · 9.4.2 DES with Python using SimPy · 9.4.3 Example application of DES in digital twins · 9.5 Finite element and field simulation · 9.5.1 Using FEM for virtual sensing in a digital twin · 9.5.2 Example application of FEA in digital twins · 9.6 Computational fluid dynamics · 9.6.1 CFD with Python for lightweight simulation · 9.6.2 Example application of CFD in digital twins · 9.7 Reduced-order models · 9.7.1 ROMs for virtual sensing · 9.7.2 An example ROM · 9.8 Summary

What you learn in chapter 9 →

Chapter 10: Digital twins in production

10.1 Selecting the right deployment platform · 10.1.1 Public cloud providers · 10.1.2 Digital twin development platforms and engines · 10.1.3 Fully managed digital twin platforms · 10.2 Security as a cross-cutting concern · 10.2.1 Expanding the threat model · 10.2.2 Identity everywhere · 10.2.3 Policy as code · 10.3 Digital twin operations · 10.3.1 Observability · 10.3.2 Model lifecycle management · 10.3.3 Change management · 10.3.4 Cost management · 10.3.5 Software dependency management · 10.4 Aligning to strategy and measuring value · 10.4.1 Operational impact · 10.4.2 User adoption and trust · 10.4.3 Financial value · 10.5 Governance, ethics, and responsibility · 10.5.1 Decision authority · 10.5.2 Accountability and ownership · 10.5.3 Ethical use · 10.5.4 Auditability and traceability · 10.6 A production readiness checklist · 10.7 Summary

What you learn in chapter 10 →

Concepts explained in this book

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