Combining Physics and Deep Learning

With the rise in compute power over the past 10 years, we have seen a sharp increase in the number of simulations. Digital twins are one such example. They are virtual replicas of a physical object or process that can be simulated in a variety of scenarios. One problem faced by digital twins is how they can combine potentially noisy empirical data with physics. In 2021, researchers at the University of Sheffield developed a very simple digital twin framework called PhysiNet to solve this problem.