CFD Simulation

Computational Fluid Dynamics (CFD) simulation aims to accurately model fluid flow using numerical methods, primarily to predict and understand complex fluid behavior in various applications. Current research heavily emphasizes integrating machine learning, particularly deep learning architectures like Graph Neural Networks (GNNs) and Convolutional Neural Networks (CNNs), to enhance efficiency and accuracy, focusing on areas such as data assimilation, in-situ training, and correcting numerical errors. This integration promises to significantly reduce computational costs and improve the speed and scalability of CFD simulations, impacting fields ranging from automotive design to medical diagnostics.