1 Bridging the physical and digital worlds
This chapter covers
- Defining what a digital twin is
- Different levels of digital twin maturity
- What digital twins are good for and when not to consider them
- How digital twins are used across industries
- Considerations when embarking on building a digital twin
Digital twins are virtual representations of physical systems that are connected to real-world data, enabling them to reflect current conditions and inform predictions about future behavior. The concept builds on decades of simulation and systems modeling, including work at NASA during the Apollo program. However, unlike traditional simulators, digital twins are distinguished by their operational data linkage to the physical system.
For years, digital twins remained accessible only to large, well-funded organizations that could afford the scarce, expensive sensors, computing capacity, and advanced analytics required to build them. That’s all changed. Today, digital twins can be built by almost anybody, with an array of low-cost sensors, high-performance, pay-as-you-go computing, and powerful artificial intelligence and machine learning tools widely available.
Some think of digital twins as detailed 3D models. while others see them as glorified dashboards or traditional simulation systems. In this book, we define a digital twin as a system that combines elements from all these interpretations, but is not restricted to any single technology.