A digital twin is a virtual representation of a real system – a building, the power grid, a city, even a human being – that mimics the characteristics of the system. A digital twin is more than just a computer model, however. It receives data from sensors in the real system to constantly parallel the system’s state.
A digital twin helps people analyze and predict a system’s behavior under different conditions. The systems being twinned are typically very complex and require significant effort to model and track.
Digital twins are useful in a wide variety of domains, including supply chains, health care, buildings, bridges, self-driving cars and retail customer personas to improve efficiency and reliability. For example, a warehouse operator can optimize a warehouse’s performance by exploring the response of its digital twin to various material handling policies and equipment without incurring the cost of making actual changes.
Even a wildfire can be represented by a digital twin. Government agencies can predict the spread of the fire and its impact under different conditions such as wind velocity, humidity and proximity to habitats, and use this information to guide evacuations.
Read the full article at The Conversation.