Digital Twins are increasing in usage but are often used in multiple contexts and in a simplified manner. Most references to the Digital Twin actually refer to a Digital shadow i.e. maintaining a digital copy of a physical object that is updated periodically. In a more complete sense, the Digital Twin concept relates to simulation and interaction of complex, multiple physical objects in a digital environment (typically for Engineering and Construction)
I am interested in the idea of Digital Twin because my teaching at the #universityofoxford applies more to AI in engineering (as opposed to say financial services).
Also, Digital Twins relate to the idea of Physics based modelling in Engineering. A wind tunnel is an example of Physics based model. Hence, one could think of a corresponding digital entity to the physical model which simulates the behavior of the model in a digital sense.
For this reason, digital twins are one of the best conceptual mechanisms for incorporating artificial intelligence into large-scale, dynamic engineering problems – especially considering existing ideas of physics-based modelling in engineering.
Digital twin technology is already used in various industrial sectors such as aerospace, infrastructure and automotive.
A paper I recently read talks about how Digital twins can be implemented through surrogate modeling.
The paper uses a discrete damped dynamic system to explore the concept of a digital twin.
An image of this idea is as below
The paper uses Gaussian process (GP) emulator within the digital twin technology is explored. GP has the inherent capability of addressing noisy and sparse data.
GP is a probabilistic machine learning technique that attempts to infer a distribution and then use that distribution to predict unknown points.
GP has two distinct advantages over other surrogate models:
Additional notes from the paper