Understanding Digital Twins

A digital twin isn’t about how it looks—it’s about what it knows and how it acts. According to IoT Analytics, the market for digital twins expanded by 71% between 2020 and 2022 set to jump from $𝟒𝟏𝟖 𝐦𝐢𝐥𝐥𝐢𝐨𝐧 𝐢𝐧 𝟐𝟎𝟐𝟐 𝐭𝐨 $𝟏.𝟓 𝐛𝐢𝐥𝐥𝐢𝐨𝐧 𝐛𝐲 𝟐𝟎𝟐𝟕 at a 𝟐𝟗.𝟒% 𝐚𝐧𝐧𝐮𝐚𝐥 𝐠𝐫𝐨𝐰𝐭𝐡 𝐫𝐚𝐭𝐞. On top of that 𝟔𝟑% of manufacturers are currently developing a digital twin or have plans to develop a digital twin. But the most interesting part? Most businesses already have the foundation for a digital twin and don’t even realize it. According to IEEE, the digital twin concept can be divided into three subcategories according to the different integration level, namely the different degree of data and information flow that may occur between the physical part and the digital copy: • 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐌𝐨𝐝𝐞𝐥: A digital version of a physical object (preexisting or planned) to correctly define a digital model with no automatic data exchange between the physical model and digital model. Examples of digital models include plans for buildings and product designs. The important defining feature is there is no form of automatic data exchange between the physical system and the digital model. This means once the digital model is created a change made to the physical object has no impact on the digital model. • 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐒𝐡𝐚𝐝𝐨𝐰: A digital representation of an object that has a one-way flow between the physical and digital object. A change in the state of the physical object leads to a change in the digital object and not vice versa. • 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧: If the data flows between an existing physical object and a digital object, and they are fully integrated in both directions, this becomes a “Digital Twin”. A change made to the physical object automatically leads to a change in the digital object and vice versa. And if you’re running a 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 𝐒𝐲𝐬𝐭𝐞𝐦 (𝐌𝐄𝐒), you might already be closer to having a digital twin than you think. A well-implemented MES should naturally create a digital twin of your manufacturing process, enabling real-time monitoring, AI-driven insights, and automated process optimizations. If your MES isn’t doing this, you might not be unlocking its full potential. 𝐅𝐨𝐫 𝐦𝐨𝐫𝐞 𝐢𝐧𝐟𝐨, 𝐜𝐡𝐞𝐜𝐤 𝐨𝐮𝐭 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/eTSdpCsW ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

"The Role of Digital Twin Technology in Bridge Engineering." With the rapid advancement of digital technologies, the construction and maintenance of bridges are evolving beyond traditional engineering methods. One of the most transformative innovations in recent years is Digital Twin Technology, which is reshaping how we design, monitor, and maintain bridges by integrating real-time data, predictive analytics, and AI-driven insights. What is a Digital Twin? A digital twin is a virtual replica of a physical bridge that continuously receives real-time data from IoT sensors embedded in the structure. These sensors monitor structural conditions, load distribution, environmental impacts, and material fatigue, creating a dynamic and interactive model that mirrors the actual performance of the bridge. This virtual model allows engineers to simulate different scenarios, detect anomalies early, and optimize maintenance strategies before actual failures occur. How Digital Twins Are Revolutionizing Bridge Engineering 1. Real-Time Structural Health Monitoring (SHM) IoT sensors collect continuous data on factors such as temperature, stress, vibration, and corrosion. AI-powered analytics process this data to identify patterns of deterioration and potential structural weaknesses. Engineers can access real-time insights from remote locations, reducing the need for frequent on-site inspections. 2. Predictive Maintenance & Cost Efficiency Traditional maintenance relies on scheduled inspections, often leading to unnecessary costs or delayed repairs. With digital twins, predictive analytics help forecast which parts of a bridge will require maintenance and when, optimizing repair schedules. This proactive approach extends the lifespan of the bridge and reduces long-term maintenance expenses. 3. Simulation & Risk Assessment Engineers can simulate extreme weather conditions, earthquakes, and heavy traffic loads to assess a bridge’s resilience. This allows for better disaster preparedness and risk mitigation, ensuring public safety. In construction projects, digital twins can be used to test different design alternatives before actual implementation. 4. Sustainability & Smart City Integration By optimizing material usage and maintenance, digital twins help reduce environmental impact. They also enable better traffic flow analysis, contributing to the development of smarter and more efficient transportation networks. Integrated with Building Information Modeling (BIM) and Machine Learning, digital twins are a key component of smart infrastructure development. Video source: https://lnkd.in/dkwrxGDE #DigitalTwin #BridgeEngineering #SmartInfrastructure #CivilEngineering #StructuralHealthMonitoring #Innovation #IoT #BIM #AIinConstruction #civil #design #bridge

The Technology Stack Behind a Real #Digital #Twin Every impressive Digital Twin demo usually hides one important question: Where does all that data actually come from? A Digital Twin isn't a single application. It's an ecosystem of technologies continuously exchanging data to model the physical world. A simplified architecture looks like this: #Layer #1 — #Physical #World Everything starts with real assets. • Buildings • Roads • Bridges • Power Lines • Wind Turbines • Factories • Water Pipelines These assets generate enormous amounts of data. #Layer #2 — #Data #Acquisition Multiple technologies observe the same asset from different perspectives. • IoT Sensors → Temperature, vibration, pressure, strain • Drone Mapping → Orthomosaics, point clouds, 3D meshes • LiDAR → High-density geometry • Satellite Imagery → Large-scale monitoring • CCTV Cameras → Visual inspection • Mobile Mapping → Street-level updates • SCADA Systems → Operational telemetry No single sensor tells the whole story. Sensor fusion creates the complete picture. #Layer #3 — #Data #Engineering Raw data is rarely usable. It must be: • Cleaned • Registered • Georeferenced • Time synchronized • Converted into common coordinate systems • Indexed • Version controlled Without this layer, your Digital Twin becomes inconsistent within weeks. #Layer #4 — #Spatial #Data #Platform This is where everything connects. Typical datasets include: • GIS Layers • BIM Models • Point Clouds • Meshes • Terrain Models • Utility Networks • Asset Inventories • Time-series Sensor Data Every object receives a unique identity. Now a bridge isn't just geometry. It's linked to inspections, maintenance logs, sensor history, drawings, documents, and operational events. #Layer #5 — #Intelligence This is where AI becomes valuable. Machine Learning models can: • Detect structural defects • Predict equipment failures • Estimate Remaining Useful Life (RUL) • Forecast maintenance costs • Detect anomalies • Simulate future scenarios • Optimize operations Instead of dashboards, you begin receiving recommendations. #Layer #6 — #Applications Finally, different teams consume the Digital Twin. • Operations • Maintenance • Engineering • Asset Management • Emergency Response • City Planning • Executives Everyone works from the same continuously updated source of truth. A mature Digital Twin is less about visualization and more about data architecture. The hardest challenge isn't rendering millions of points in 3D. It's integrating dozens of heterogeneous systems into a reliable, real-time representation of reality. That's what transforms a collection of datasets into a Digital Twin. #DigitalTwin #AI #MachineLearning #IoT #GIS #LiDAR #DroneMapping #ComputerVision #SpatialComputing #SmartInfrastructure #Engineering #DataEngineering #AssetManagement

I had the opportunity to visit the Iron Ore Company of Canada recently with Rio Tinto Board colleague Dean Dalla Valle and the new CEO of Rio Tinto Iron Ore, Matthew Holcz. The Iron Ore Company of Canada (IOC) operations are integrated across a mine and processing plant in Labrador City, Newfoundland and Labrador, and a port and stockpile in Sept-Îles, Quebec. A 418 km railway (the Quebec North Shore and Labrador Railway) joins the two parts; taking iron ore from mine to port, and equipment and supplies from port to mine. As well as using rail operations to move ore to the port, rail operations are also used within the mine to move mined ore to the processing plant. The ~10km long Automatic Train Operation that runs within the mine was one of the first automatic train systems in the world. The global iron and steel industry currently contributes about 8% of global carbon emissions. The high-grade, low impurity iron ore produced by IOC is an essential raw material for new iron-making technologies, including the evolving green steel market. In a story very similar to the Aluminium Company of Canada- ALCAN (which I have written about previously), IOC’s history is an incredible story of pioneering project building in some of the most remote and rugged landscape in the world. The company was formed in 1949 after four years of exploration and development. The then President of IOC, George Humphrey, coined the slogan “iron ore by ‘54” and construction of the 418 km railway started in 1951. Three years later, in 1954, the first shipment of ore left Sept-Îles for Philadelphia. Seventy-one years later the operation is still going strong.   The rail line is now managed from a state-of-the-art remote operations centre in Sept-Îles. The length and remote location of the rail line means that management and timing of maintenance is especially important to overall productivity. It was terrific to hear from Rémi Robichaud and Francois Raymond on their push for innovation, and their use of digital and AI in rail operations today. They have built a digital twin for the entire system. This allows the team to optimise current operations for safe maintenance operations and maximum production, and also to run complex simulations to plan and maximise future production and maintenance activities. Thanks to Mike McCann, Ryan Harnden, Zonika Ramsey, Mark Arkell, Lachlan Cumming, José Riopel and the whole IOC team for a great visit. I attach some photos from the visit.

Six months off a clinical trial is worth around $90–150M. Novartis is trying to claim it with a simulator. I heard the story today from Chinmay Bhatt, Head of Strategy at Novartis, at the Galien Forum in London. The maths is brutal. Tufts now puts a single day of drug-development delay at roughly $500k–$800k in lost sales — higher in oncology. Run that across six months and the prize is a nine-figure number, per trial. 🤔 But the slow part isn’t where you’d think. Before a trial runs, clinicians, statisticians, and operations teams have to agree on endpoints, sample size, sites, and timelines. According to Chinmay, those opening operational discussions alone used to take around six months. 🤖 Novartis’s answer is a digital twin of the trial — an Intelligent Decision System that simulates the clinicians, the statisticians, and the operations team. It runs the what-if discussion in hours, not months. 💡 The bottleneck wasn’t discovery or biology. It was the meeting — the cost of getting expert humans to agree. That’s the layer AI is quietly eating first: not the lab bench, but the planning table. 👉 If AI can simulate the planning meeting, which part of the trial does it compress next — recruitment, monitoring, or the read-out?

Digital twins are increasingly being developed to guide high-stakes decisions, from medicine to climate and energy. Many ML-based digital twins are trained to minimise simulation error, but does this always lead to correct decision making? I'm delighted to share another #ICML2026 paper: "DT²: Decision-Targeted Digital Twins" (read here: https://lnkd.in/eaD5v4Fh) Led by Harry Amad, this work introduces DT²: a new framework for training digital twins around the decisions they are ultimately designed to support. Training to minimise simulation errors, like negative log-likelihood or mean-squared error, can under-emphasise the importance of particular parts of the transition distribution that greatly affect the ranking of candidate policies. DT² shifts the focus of the model during training, to be aware of the dynamics most important for downstream decision making. DT² uses off-policy evaluation methods, on offline data to estimate how candidate policies compare. These proxy rankings are then built into the digital twin’s training objective. The result is a model trained not only to simulate trajectories, but to preserve the policy orderings needed for decision support. Across six continuous-control environments and five digital twin architectures, DT² consistently improved decision support: >54% lower average decision regret >47% higher rank correlation >better policy ranking than conventional DT training We evaluated DT² in a cancer treatment case study using digital twins. Compared with conventional digital twin training, DT² achieved: >lower decision regret >higher rank correlation >better ranking of both seen and unseen treatment strategies while maintaining nearly the same simulation fidelity. Takeaway: the goal of a digital twin is not just to produce high-fidelity simulations but it is to guide the next decision. With DT², we make DTs aware of the kind of policies they will be used to deliberate over during training, improving their ability to provide decision support.

𝐅𝐮𝐭𝐮𝐫𝐢𝐬𝐭𝐢𝐜 𝐂𝐀𝐃: 𝐓𝐡𝐞 𝐍𝐞𝐱𝐭 𝐄𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐨𝐟 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐃𝐞𝐬𝐢𝐠𝐧 CAD is no longer just about drawing parts — it’s evolving into an intelligent design partner. With the rise of AI-driven #CAD, generative design, and cloud-based collaboration, engineers are moving from manual modeling to smart design automation. Imagine software that suggests optimal geometries, reduces weight automatically, predicts failures, and even generates manufacturable designs within minutes. The future of CAD will likely include: 🔹 AI-assisted modeling that predicts design intent 🔹 Generative design creating multiple optimized solutions 🔹 Real-time simulation during modeling 🔹 Cloud CAD enabling global collaboration 🔹 Digital Twins connecting #CAD directly to real-world performance For #mechanical, #automotive, and #aerospace engineers, learning futuristic #CAD tools is no longer optional — it's becoming a career advantage. The engineers who adapt early will design faster, smarter, and more efficiently than ever before. Join #MECHHUB , India's only exclusive community for Mech, Auto, Aero engineers to stay updated and learn Mech+AI skills. MechHub: nas.io/mechhub

Energy companies are building digital twins of entire power networks. Virtual replicas that run thousands of what-if scenarios before anything goes wrong in the real world. A severe storm is heading toward your grid. Equipment showing early signs of fatigue. A sudden demand spike in a region you weren't watching. The digital twin tests it all. Identifies the weak points. Let's operators redesign their response before the event actually happens. This approach transforms critical infrastructure from reactive to proactive: failures are prevented rather than managed. I think about this every time I see a company running scheduled maintenance on a calendar instead of on data. Predictive AI can flag equipment issues weeks before breakdown by reading sensor patterns that no human inspection team would catch. But most organizations are still budgeting for the old way because that's what they've always done. #DigitalTwins #EnterpriseAI #PredictiveMaintenance #EnergyTransition #SmartGrid #IndustrialAI #AssetManagement #AIAdoption #OperationalExcellence #Infrastructure

Harnessing Geodata and Digital Twins for Sustainable and Climate-Resilient Cities 🌍 In today's rapidly changing world, urban areas face significant challenges due to climate change. Extreme weather events, rising temperatures, and increasing urbanization demand innovative solutions to ensure cities remain sustainable and resilient. By leveraging the power of geodata and digital twins, these challenges can be addressed head-on. Advanced aerial surveying techniques provide highly current and detailed geodata essential for creating digital twins. These virtual representations of cities enable precise analysis and modeling of urban processes, allowing for the development and monitoring of effective measures against climate change impacts. 🔍 Key Applications: 🔸 Tree Cadastres: Mapping urban trees to manage green spaces and mitigate heat islands. 🔸 Solar Potential Analysis: Assessing rooftops for solar energy installations to promote renewable energy. 🔸 Temperature Modelling: Model temperature distribution in cities 🔸 Sealed Surface Detection: Identifying impermeable surfaces to improve stormwater management and reduce urban overheating. By integrating AI with geospatial data, valuable insights can be extracted to help cities implement targeted and sustainable solutions. This work demonstrates how technology and data can empower urban planners and decision-makers to create climate-resilient cities for the future. Let's work together to build a sustainable and resilient urban environment! 🌱🏙️ 💡 Comment | Like | Share 👉 Follow me (Dr. Uwe Bacher) for more insights on exciting topics from the world of geospatial #Geodata #DigitalTwins #Sustainability #ClimateResilience #UrbanPlanning #Sensor2Solution #AerialMapping

Modern AI factories can no longer be designed or operated like traditional data centers. When tens of thousands of clusters spin up simultaneously, an AI factory behaves like a single, highly volatile computer. Traditional, reactive HVAC systems are simply too slow to handle the instantaneous thermal spikes. The system throttles, and efficiency plummets. NVIDIA Omniverse DSX Blueprint fixes this by turning the digital twin into a live operational control loop using OpenUSD. The engineering breakdown: - Predictive AI Agents: Operators train reinforcement learning agents inside the digital twin to simulate facility thermodynamics in real time. The agent predicts and mitigates thermal spikes before they physically happen. - Silo-Free Commissioning: Mechanical designs (from platforms like PTC Windchill) feed directly into the twin. Teams simulate fluid dynamics and airflow efficiency long before breaking ground. - Shifting the Power Equation: By relying on predictive AI to eliminate dangerous thermal margins, operators can safely run facilities hotter. This slashes cooling energy and reallocates that raw power directly back into the IT compute load. In a power-constrained market, traditional PUE is an outdated metric. The new standard for infrastructure design is Tokens per Watt, and the digital twin is the software stack required to maximize it. full video : https://lnkd.in/gdsZsWq5 Download the blueprint for free: https://lnkd.in/gAZUf99w #PhysicalAI #DataCenterInfrastructure #NVIDIAOmniverse #DSXBlueprint #OpenUSD #ThermalEngineering #ComputeEfficiency