1 Bridging the physical and digital worlds
Digital twins are living digital counterparts of physical systems that continually ingest real-world data to reveal current conditions, forecast future behavior, and, when needed, act back on the physical world. Once the domain of space programs and capital-intensive industries, they are now widely accessible thanks to affordable sensors, pay-as-you-go cloud computing, and powerful AI/ML tools. A twin is not tied to any single technology or visualization—it can be a rich 3D scene, a dashboard, or a “headless” service—so long as it is objective-driven, synchronized with reality, and used to make better, faster decisions.
Modern twins are enabled by IoT devices and efficient networks, scalable cloud and low-latency edge computing, and AI/ML for pattern recognition, forecasting, and anomaly detection—augmented increasingly by agentic AI that can plan and coordinate actions. Capabilities span a maturity spectrum: descriptive (static representations), informative (real-time overlays), predictive (anticipating failures or travel times), comprehensive (simulation fused with live data through techniques like data assimilation), and autonomous (closing the loop with adaptive, goal-aware control). As maturity grows, twins evolve from showing what is happening to exploring what could happen and ultimately deciding what to do.
The payoff is broad and tangible: faster product development with fewer physical prototypes, lower costs via predictive maintenance, improved performance through continuous optimization, lifecycle support from design to operations, and safer, more effective training. Twins are delivering value across manufacturing, energy and utilities, mining, automotive, agriculture, critical infrastructure, and smart buildings and cities. Building one starts with a clear definition of success, then identifying and collecting the right data, transporting and contextualizing it, modeling entities and behavior, running analytics and simulations, and closing the loop with real-world actions—ideally proven first through a small pilot. Key challenges to anticipate include data quality and context gaps, OT/IT integration, skills shortages, and strategic build-vs-buy decisions, often best addressed with a hybrid approach.
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 is continuously updated with real-world data. It helps people understand current conditions and structure, monitor performance, simulate scenarios, and make decisions against clear objectives. Advanced twins can also act on the physical system by sending instructions to adjust or optimize behavior.How is a digital twin different from a 3D model, dashboard, or simulator?
- A 3D model visualizes form; a dashboard shows current metrics; a simulator tests “what-if” scenarios.- A digital twin can combine all three, but is defined by continuous synchronization with real-world data, a contextual model of entities and relationships, predictive capabilities, and—at higher maturity—closed-loop actions. Some twins are “headless,” focusing on data, analytics, and decisions without visuals.
Which technologies are enabling modern digital twins?
- IoT: low-cost sensors and actuators; connectivity via 5G and LPWAN (e.g., LoRaWAN) for long-range, low-power telemetry.- Cloud computing: elastic storage/compute, managed analytics/ML, pay-as-you-go scaling.
- Edge computing: low-latency local analytics and filtering when timing or bandwidth matters.
- AI/ML: anomaly detection, forecasting, optimization; accessible via cloud and on-device models.
- Agentic AI: autonomous software agents that reason, plan, and act across a twin.
What are the five maturity levels of digital twins?
- Descriptive: a static digital representation (e.g., a map or 3D model).- Informative: live data overlaid on the model (e.g., traffic on maps, dashboards).
- Predictive: forecasts future states using history and models (e.g., travel-time prediction, equipment failure risk).
- Comprehensive: integrates simulations with real-world data via data assimilation to explore scenarios (e.g., weather/climate twins).
- Autonomous: closes the loop to take actions, often with agentic AI (e.g., smart thermostat optimizing comfort and energy).
What are digital twins good for?
- Accelerating product development through virtual prototyping and “what‑if” simulation.- Reducing costs via predictive maintenance (e.g., jet engine health and service planning).
- Optimizing performance and operational efficiency with real-time insights and thresholds (e.g., network KPIs).
- Supporting the full asset lifecycle from planning/design to construction and operations (e.g., large infrastructure projects).
- Training and simulation, including immersive and high-fidelity replicas (e.g., anatomical models, operational simulators).
How are digital twins used across industries?
- Mining, energy, and industrial: process optimization, safety, and training in hazardous environments.- Automotive: design simulation (e.g., aerodynamics) and vehicle state management, especially for connected EVs.
- Agriculture: optimizing inputs and yields using weather, soil, and machine health data.
- Infrastructure: grid balancing, flood protection, and system-level orchestration of critical assets.
- Smart buildings/cities/states: energy vs. comfort optimization, mobility planning, and operations at multiple scales.
- Human-centric twins: personalization and recommendations; requires strong privacy and ethics.
How do I get started building a digital twin?
1) Define the physical system and specific objectives.2) Identify required data (static sources, sensors, cameras, external APIs).
3) Connect and transport data reliably into your digital environment.
4) Build the model: visual, mathematical, and relationship/graph data layers.
5) Run analytics and simulations to generate insights and forecasts.
6) Close the loop with actions back to the physical world (alerts, control, automation). Start with a small pilot to validate value and gain buy-in.
What are the key challenges and considerations?
- Clear success metrics that drive behavior change, not just dashboards.- Data quality: calibration drift, gaps, noise, inconsistent units/protocols, and vendor data lock-in.
- Data context: link measurements to locations, assets, and types; build a shared semantic/graph model.
- Skills gap: device/OT, networking, data engineering, ML, and domain expertise must converge.
- Connectivity and latency: decide cloud vs. edge placement for reliability and speed.
- Privacy and ethics: especially for human-centric or sensitive operational data; govern access and usage.
How does an autonomous digital twin differ from traditional OT/SCADA control?
- Traditional OT/SCADA: fixed rules/heuristics within bounded conditions; proprietary, siloed, focused on stability and real-time control.- Autonomous twin: predicts future states, reasons across competing objectives, learns and adapts, and can generate new control strategies; integrates broader enterprise data and agentic AI; aims for holistic optimization, not just local control.
Should we build or buy a digital twin platform?
- Custom build: maximum flexibility and data ownership; requires more expertise, time, and maintenance.- Off-the-shelf: faster deployment and proven capabilities; constrained by existing features and potential data sovereignty concerns.
- Hybrid (build on PaaS/customize): quicker than full custom while allowing differentiation; introduces vendor dependency and upgrade complexity. Choose based on criticality, timeline, in-house skills, and long-term data strategy.
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