Digital Twin is a dynamic virtual replica of a physical object, system, or process, continuously synchronized with its real-world counterpart through sensor data to mirror its state, behavior, and full lifecycle.

A digital twin pairs a 📝Physical Twin with a software model that updates in near real time, so the virtual version reflects the physical one as conditions change. Unlike a static simulation or CAD model, the twin is bound to live data: it ingests readings from sensors, controllers, and operational systems to stay continuously aligned with the asset it represents across that asset's entire lifecycle.

The binding to live data is what makes the twin useful. Operators use it to observe, diagnose, predict, and optimize — monitoring status, detecting anomalies, forecasting behavior, and testing changes virtually before committing them to the physical system. Because experiments run against the model rather than the real object, organizations can evaluate alternatives, schedule maintenance, and avoid failures without disrupting production.

Digital twins depend on 📝Internet of Things devices for the sensor streams that keep them current, and increasingly on machine-learning models that turn those streams into predictive insight. The concept originated in product engineering and aerospace and expanded through the 2010s and 2020s across manufacturing, infrastructure, healthcare, and urban planning, where twins model everything from individual machines to entire factories and cities.