Flow Field

Flow field research focuses on understanding and predicting the movement of fluids, aiming to accurately model and simulate complex flow patterns in various contexts. Current research heavily utilizes machine learning, employing architectures like convolutional neural networks, graph neural networks, and diffusion models to estimate, reconstruct, and predict flow fields from often incomplete or noisy data, incorporating physics-based constraints where possible to improve accuracy and efficiency. These advancements have significant implications for diverse fields, improving the accuracy and speed of simulations in aerodynamics, biomedical engineering, and other areas where understanding fluid dynamics is crucial.