At the intersection of Reduced Order Modelling and Discrete Loss Minimization: Can complicated, high-Re incompressible flow models become edge computable?

Digital twin technology is growing widely among a number of research communities. As with any new digital tool, its usefulness is often directly proportional to speed. As a result, large-scale direct 2D and 3D incompressible flow calculations are not widely considered in digital twins, regardless of the application space. There have been a number of advancements in model reduction metholodigies in recent years, to include submerged surrogates and autodifferentiation-based solvers. Herein we present a hybrid technique capable of accurately assimilating temperature and pressure data into a generalizable convection-driven flow model without the use of PINNs.