When applied to healthcare, the Digital Twin – a virtual version of real-life objects that can be used to predict how that object will perform – could predict how a patient’s disease will develop and how patients are likely to respond to different therapies. It is also of huge benefit in aerospace, where, for example, the technology will be needed to monitor and control thousands of drones, ensuring that they are maintained, have efficient and safe flight plans and can automatically adapt to changes in conditions, such as weather, without the need for human interaction.
However, current Digital Twins are largely the result of bespoke technical solutions that are difficult to scale.
The authors say that these ‘use cases’ place new demands on the speed, robustness, validation, verification and uncertainty quantification in Digital Twin creation workflows. Achieving Digital Twins at scale will require a drastic reduction in technical barriers to their adoption.
+INFO: University of Cambridge