Fluid Simulation

Fluid simulation aims to accurately and efficiently model the movement of fluids, crucial for diverse applications from engineering design to climate modeling. Current research heavily emphasizes the use of machine learning, particularly neural networks (including transformers, convolutional neural networks, and graph neural networks), to create faster and more adaptable solvers than traditional computational fluid dynamics methods. These machine learning approaches are being developed and benchmarked using large datasets of simulated fluid flows across various geometries and conditions, focusing on improving accuracy, generalization, and handling of complex interactions like fluid-solid coupling. The resulting advancements promise significant speedups and improved predictive capabilities for a wide range of scientific and engineering problems.