Flow Dynamic

Flow dynamics research focuses on accurately modeling and predicting fluid behavior, aiming to improve efficiency and understanding in diverse applications. Current efforts leverage machine learning, particularly deep learning architectures like diffusion models and physics-informed neural networks (PINNs), often combined with techniques like modal decomposition to reduce computational complexity and improve prediction accuracy for both simple and turbulent flows. These advancements are impacting various fields, from optimizing energy systems and improving biomedical simulations to enhancing the accuracy of image analysis in medical diagnostics.