Flow Field Prediction

Flow field prediction aims to rapidly and accurately estimate fluid flow patterns, circumventing the computational expense of traditional methods like Computational Fluid Dynamics (CFD). Current research heavily utilizes deep learning, employing diverse architectures such as implicit neural representations, graph neural networks, and physics-informed neural networks, often incorporating features derived directly from the underlying mesh or geometry. These advancements enable faster design iterations in various engineering applications, including aerodynamic optimization of vehicles and wind turbines, and offer more efficient analysis of complex fluid systems.