Physic Informed Flow

Physics-informed flow leverages machine learning to improve the accuracy and efficiency of fluid dynamics simulations, focusing on integrating physical laws into model architectures. Current research emphasizes using generative models, such as normalizing flows and GANs, alongside data assimilation techniques like Ensemble Kalman Filtering, to learn complex flow behaviors and quantify uncertainties. This approach enhances the predictive power of computational fluid dynamics, particularly for turbulent flows and challenging scenarios like separated flows, with applications ranging from traffic modeling to more accurate simulations of complex physical systems.