Fluid Flow

Fluid flow research focuses on understanding and predicting the movement of fluids, aiming to improve modeling accuracy and efficiency across diverse applications. Current research emphasizes the development and application of machine learning techniques, including physics-informed neural networks (PINNs), graph neural networks (GNNs), and variational autoencoders (VAEs), to solve complex fluid dynamics problems and enhance super-resolution capabilities. These advancements are improving the accuracy and speed of simulations, enabling better predictions in areas such as weather forecasting, aerospace engineering, and biomedical applications, while also addressing challenges like limited data availability and the need for interpretable models.