Digital Twins in Action

A digital twin is more than a 3D model: it connects a representation of a real system with the data and analysis needed to understand how that system behaves. In Digital Twins in Action, Greg Biegel takes readers through the engineering choices behind building one, from defining a useful goal to handling sensors, data, visualization, simulation, and intelligent decision-making.

The book keeps its focus practical, using a home-scale digital twin as a recurring example while also considering applications across industries. Its central question is a useful one for any project: what should the twin help you understand or improve?

Start with the real-world problem

Early chapters introduce digital-twin concepts, capability levels, possible uses, and common implementation challenges. Biegel then turns to project objectives and the work of translating a physical environment into a digital representation. Readers encounter information sources such as photographs, video, engineering documents, and historical records, alongside 2D and 3D models and spatial reference systems.

Connect sensors, systems, and data

The practical path continues through sensor selection, communication technologies, edge processing, data collection, and device management. From there, the book examines the varied information a twin may need to bring together—including time-series, spatial, reference, and unstructured data—and the storage, ingestion, integration, governance, and quality considerations that come with it.

Give the model context

Measurements become more meaningful when they are connected to the things, relationships, and conditions they describe. Chapters on context, ontologies, knowledge graphs, standards, and interoperability address how to build a model of reality that can support meaningful queries and applications rather than simply collect disconnected data.

Make the twin visible—and useful

Separate chapters explore 2D dashboards and visualizations as well as spatial 3D models and extended reality. The book then moves into data pipelines, anomaly detection, predictive modeling, machine learning, generative AI, and agentic systems. Simulation topics include continuous and discrete-event approaches, with production concerns such as security, operations, governance, and measuring value also in view.

A hands-on route for technical readers

Developers, data engineers, and architects with basic machine-learning knowledge will find a structured view of the full digital-twin stack, from sensing and data architecture through modeling and deployment. The appendices add focused projects on a LoRaWAN network, a custom IoT sensor, and 3D capture using photogrammetry.

For readers evaluating or building a digital twin, this book offers a grounded way to think through the components—and the decisions that connect them.