Accelerated and data-efficient flow prediction in stirred tanks via physics-informed learning

ArXi:2605.07444v1 Announce Type: cross The simulation of fluid flows is computationally expensive due to the complexity of its governing partial differential equations. Machine learning models offer a potential surrogate, enabling learning from simulations and significantly faster predictions of flow fields. However, these models require large