Flow Simulation

Flow simulation aims to accurately and efficiently model fluid dynamics, crucial for numerous scientific and engineering applications. Current research heavily emphasizes the development and application of machine learning models, including neural operators, graph neural networks, and diffusion models, to create fast and accurate surrogate models for computationally expensive numerical solvers. These data-driven approaches are being explored for various flow regimes and geometries, focusing on improving accuracy, generalization capabilities, and computational efficiency. The resulting advancements promise significant improvements in design optimization, control systems, and the understanding of complex fluid phenomena.