General Flow

General flow, encompassing diverse phenomena from fluid dynamics to data transformations, focuses on modeling and manipulating the movement or transformation of entities across space, time, or other dimensions. Current research emphasizes developing novel algorithms and model architectures, such as normalizing flows, generative adversarial networks, and physics-constrained neural networks, to reconstruct, predict, and control these flows from often incomplete or noisy data. This work has significant implications for various fields, including robotics, computer vision, and scientific computing, by enabling more accurate modeling, efficient data analysis, and improved solutions to inverse problems.