Digital Twin
A real-time virtual representation of a physical system, process, or product that is continuously updated through sensor data.
A digital twin is a real-time virtual replica of a physical system – enabling simulation, prediction, and optimization without real experiments.
Explanation
Digital twins combine IoT sensor data, simulation, and ML for prediction, optimization, and monitoring. Applications: manufacturing, building management, supply chain, urban planning.
Marketing Relevance
Enables predictive maintenance, process optimization, and what-if analyses without real experiments – saves costs and reduces risks.
Common Pitfalls
High initial investment, sensor data quality critical, model calibration complex, data security risks.
Origin & History
Michael Grieves coined the term in 2002 at the University of Michigan. NASA used digital twins for Apollo missions (precursor). GE introduced digital twins for jet engines in 2016. NVIDIA Omniverse (2021) democratized creation.
Comparisons & Differences
Digital Twin vs. Simulation
A simulation is a one-time model; a digital twin is continuously updated through real-time sensor data.