Digital Twin

The term digital twin (sometimes used interchangeably with "synthetic environment") refers to an in-silico, computer simulated analogue to a real-world system. It is often used to describe computer simulations that mirror real-world biological systems, engineering projects (like jet engines, wind turbines, or vehicles), and architectural spaces, as well as individual factories and warehouses within larger supply chains.

These "digital twins" (individual entities or entire simulation models) mirroring real-world counterparts can be placed in different virtual environments, and have their parameters modified, to allow them to be inexpensively and safely examined under a wide range of conditions, often exceeding the number and range of environments affordable or practical to test in the physical, offline world.

These simulations can also be used to generate synthetic data, perform low-cost tradespace analyses, and generate risk assessments (e.g. by introducing simulated stochastic failures).

All of these things can be fed back into the design of real-world systems, allowing them to be tested and improved online, before being built "in the real world" offline.

HASH allows for real-time sensor and other streaming data, as well as static datasets and domain expert assumptions to be layered into digital twins in order to ensure they remain up to date. Digital twins are very valuable in many engineering, manufacturing and logistics contexts, as well as any other environment where real-world experiments with strategy, design or layout changes are very costly, but where even small improvements can lead to large cost savings or quality and performance improvements.