Data-driven digital twins: Where statistics meets physics

<p>A digital twin is a virtual representation of a physical process or product. It is used to simulate how its physical counterpart will perform throughout its lifecycle. Digital twins can evolve with the real-time flow of data from a real-world system, helping developers understand and control the performance of the physical process or product. They also help developers overcome process uncertainties and deal with unforeseen fluctuations as they occur.</p> <p>Associate professor Jan Kloppenborg M&oslash;ller, Dr Goran Goranović and Professor Henrik Madsen from the Technical University of Denmark (DTU) and Dr Per Brath from Grundfos (currently at Danfoss) have developed a digital-twin methodology called the&nbsp;<em>Stochastic Greybox Modelling and Control</em>&nbsp;that accounts for uncertainties and random changes. They demonstrate its suitability for industrial use with an example application to fluid ultrafiltration.</p> <p><a href="https://medium.com/@ResearchFeatures/data-driven-digital-twins-where-statistics-meets-physics-38684a1e328d"><strong>Click Here</strong></a></p>