A digital twin is a virtual replica of a physical object, process, or system that updates in real time from sensor data. Engineers interact with the twin as they would with the real asset, but without safety, cost, or downtime constraints. The concept applies to a single turbine blade, an entire factory floor, or a city's infrastructure. Manufacturing plants run virtual assembly lines to test robot layouts and predict bottlenecks before retooling. Energy utilities feed sensor data from turbines into twins to spot wear early and schedule maintenance at the right moment. City planners model traffic networks, water systems, and building energy use to test policy changes without disrupting daily operations. Digital twins create a feedback loop. The virtual model guides real-world decisions. Those decisions produce new data that refines the model. General Electric, Siemens, and NVIDIA Omniverse are major platforms in this space. As more devices generate real-time data through IoT, digital twins are becoming central hubs for predictive analytics and automated control.
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