Flow Field Reconstruction

Flow field reconstruction aims to accurately determine the velocity and pressure distribution within a fluid flow, often from incomplete or noisy measurements. Current research emphasizes developing robust and generalizable methods, employing techniques like Bayesian inverse problems, physics-informed diffusion models, and graph neural networks to improve accuracy and reduce reliance on specific training data. These advancements are significant for accelerating computational fluid dynamics simulations and enhancing the analysis of complex flows in various scientific and engineering applications, such as cardiovascular modeling and turbulence research.