Picture this: A delivery drone suffers some minor wing damage on its flight. Should it land immediately, carry on as usual, or reroute to a new destination? A digital twin, a computer model of the drone that has been flying the same route and now experiences the same damage in its virtual world, can help make the call.

Digital twins are an important part of engineering, medicine, and urban planning, but in most of these cases each twin is a bespoke, custom implementation that only works with a specific application. Michael Kapteyn SM ’18, PhD ’21 has now developed a model that can enable the deployment of digital twins at scale — creating twins for a whole fleet of drones, for instance.

A mathematical representation called a probabilistic graphical model can be the foundation for predictive digital twins, according to a new study by Kapteyn and his colleagues in the journal Nature Computational…

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