1 Bridging the physical and digital worlds
Digital twins are living digital representations of physical systems that stay synchronized with real-world data to explain current conditions, forecast what comes next, and, in many cases, act back on the physical world. Once reserved for space agencies and large industrial players, they are now broadly accessible thanks to inexpensive sensors, high-bandwidth and low-power networks, elastic cloud and edge computing, and readily available AI/ML. The central idea is purpose over tooling: build a model that mirrors the structure and state of a real asset to answer specific questions and drive decisions, enabling many people to observe, analyze, and experiment remotely and simultaneously.
These systems are powered by IoT devices and communications that stream telemetry into cloud/edge pipelines, where AI/ML filters signals from noise, detects anomalies, and predicts future states; emerging agentic AI extends this by planning and executing actions autonomously. In industry, digital twins augment traditional OT (PLC/SCADA) by unifying sensor data, simulations, and optimization, yielding gains from design through operations. Their maturity spans five levels: descriptive (static models), informative (live data overlays), predictive (forecasting and early warning), comprehensive (simulation with data assimilation), and autonomous (closed-loop control). Real-world patterns range from map- and dashboard-style twins to predictive maintenance in heavy equipment, large-scale weather and climate modeling, and home energy optimization via smart thermostats.
The benefits are tangible: faster product development through virtual prototyping; lower costs and downtime via predictive maintenance; continuous operational optimization; lifecycle support from planning and construction to operations; and safer, more effective training and simulation. Adoption is broad—manufacturing, energy and process industries, automotive, agriculture, infrastructure, and the built environment at building, city, and regional scales. Successful initiatives define measurable outcomes, map required data, connect and contextualize sources, build structural and behavioral models, run analytics and simulations, and close the loop with informed actions. Common challenges include incomplete or low-quality data, missing context, scarce cross-disciplinary skills, and build-vs-buy trade-offs; a pilot-first approach and pragmatic hybrid architectures help de-risk and accelerate value.
A screenshot shows the output from a 3-axis accelerometer in a modern iPhone in the phyphox app ( https://phyphox.org/) showing data indicating the owner is walking with their phone in their pocket. Such sensor data enables a digital twin to mirror and interpret real-world motion and behavior in real time.
The five categories of digital twin.
Google Maps view of lower Manhattan, a familiar example of a descriptive digital twin. Map data © 2025 Google. Google Maps is a trademark of Google LLC.
Google Maps view of lower Manhattan showing a photorealistic 3D view of the built environment provides a more detailed example of a descriptive digital twin. Imagery © 2025 Google, Map data © 2025, Map data © 2025 Google.
A directed graph model of a small subset of lower Manhattan as represented in Google Maps, with vertices representing intersections and edges representing roads with direction of travel.
Google Maps view of lower Manhattan with realtime traffic congestion data overlaid is an example of an informative digital twin. Imagery © 2025 Google, Map data © 2025, Map data © 2025 Google.
Information about the physical environment represented in a dashboard forms the basis of many informative digital twins.
An example of a predictive digital twin provided by Google Maps showing predicted travel time. Map data © 2025 Google.
What Netflix thinks I would like to watch next based on its representation of my preferences learned through past shows I have watched.
An example of a comprehensive digital twin—a view of indicators relevant to wind energy including hourly wind speed distribution with its changes at the multi decadal scale to help improve wind farm design generated with the Climate Change Adaptation Digital Twin, part of Destination Earth. Image © ECMWF. Licensed under CC BY 4.0.
The interface to a Nest smart thermostat showing how it takes action based on your physical location. Eco mode is enabled based on presence sensing via the location of mobile phones in the household. Image © Thomas Smailus, Ph.D. P.E. Reproduced with permission.
High level architecture of a digital twin showing how data about the real world is collected, stored, and processed to make decisions and affect outcomes.
Summary
- Recent advances in IoT, cloud computing, and AI/ML have made the technology required to build a digital twin widely available.
- A descriptive digital twin provides a static digital representation of reality.
- An informative digital twin integrates data streams from the real world, regularly updating the digital representation.
- A predictive digital twin forecasts what the state of the physical system might be in the future, based on an understanding of the past.
- A comprehensive digital twin simulates possible future states of the physical system, using data assimilation to update mathematical models with data from the physical system.
- An autonomous digital twin closes the loop between the physical and digital realms by taking actions in the physical world based on analytics, predictions, or simulations in the digital representation.
- Before building a digital twin, you must be clear about the outcomes you are looking to achieve, what skills you will need, and whether you intend to build it from scratch, buy off the shelf capabilities, or a combination of the two.
FAQ
What is a digital twin?
A digital twin is a digital representation of a physical system that stays synchronized with real-world data. It helps users understand current conditions, monitor performance, and simulate scenarios to make decisions against clear objectives. Crucially, it can also act on the physical system by sending instructions to adjust or optimize behavior.Why are digital twins feasible and popular now?
Low-cost, compact sensors; ubiquitous connectivity (5G, LPWAN/LoRaWAN); pay-as-you-go cloud and edge computing; and accessible AI/ML tools have removed the historic barriers (expensive hardware, scarce expertise). What once required NASA-scale resources is now attainable by startups, hobbyists, and enterprises alike.How is a digital twin different from a 3D model, dashboard, or simulator?
A digital twin can include elements of all three but is not limited to any one of them. Its distinguishing features are live data synchronization with the physical world, a structural model of entities and relationships, predictive/simulation capability, and (at higher maturity) the ability to take action. Some twins are “headless,” focusing on data and decisions without visualization.What are the five levels of digital twin maturity?
- Descriptive: A static digital representation (for example, maps or 3D views).- Informative: Live data overlays the model to show current state (for example, traffic on maps).
- Predictive: Forecasts future states using rules or ML (for example, predictive maintenance, travel-time estimates).
- Comprehensive: Simulates “what-if” scenarios and assimilates real-world data into models (for example, weather prediction).
- Autonomous: Closes the loop by taking actions in the real world, often via agentic AI (for example, smart thermostats).
What technologies enable modern digital twins?
- IoT: Affordable sensors/actuators and efficient networks (5G, LPWAN/LoRaWAN) for long-range, low-power data collection.- Cloud and edge: Elastic compute/storage for pipelines, simulations, and analytics; edge for low-latency decisions and bandwidth savings.
- AI/ML: Anomaly detection, forecasting, optimization—available via managed and pre-trained models.
- Agentic AI: Software agents that plan, reason, and execute workflows to optimize objectives autonomously.
What benefits do digital twins deliver?
- Accelerate product development through virtual prototyping and scenario testing.- Reduce costs via predictive maintenance and fewer unplanned outages.
- Optimize performance and efficiency in real time across assets and processes.
- Support the full asset lifecycle from design and construction to operations.
- Enable training and simulation, including immersive and risk-free practice.
Which industries use digital twins today?
Capital-intensive sectors (mining, energy, industrial processing), automotive (design and connected vehicles), agriculture (yield optimization and weather integration), critical infrastructure (power grids, flood protection), and the built environment (smart buildings, cities, and regional twins). Examples span Google Maps (city-scale context), Rolls-Royce Intelligent Engine (predictive maintenance), and national-scale climate/weather twins.How do autonomous digital twins differ from traditional OT/SCADA/DCS control?
Traditional OT systems use fixed, predefined logic within bounded conditions and often operate in isolated, proprietary environments. Autonomous twins integrate broader data, predict future states, reason about competing objectives, and adapt strategies over time—often via agentic AI—then act physically (actuators) or electronically (alerts/workflows) to optimize outcomes.What does a typical digital twin architecture and data flow include?
1) Define objectives and scope. 2) Identify and collect required data (static sources, sensors, cameras, APIs). 3) Ingest and transport data to the digital environment. 4) Build models (visual, mathematical/physics, and contextual graphs of entities/relationships). 5) Run analytics and simulations. 6) Close the loop by taking actions that affect the physical system.What should I consider when starting my first digital twin?
- Define success with measurable outcomes that drive behavior change; start with a small pilot.- Ensure data quality (calibration, completeness, reliability) and robust connectivity.
- Add context: consistent identifiers, metadata, and relationships across systems.
- Address the skills gap across hardware, networking, software/data engineering, ML, and domain expertise.
- Decide on build vs buy vs hybrid, balancing speed, flexibility, differentiation, and data sovereignty.
- For human-centric twins, prioritize privacy and ethical use.
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