Incompressible Fluid

Incompressible fluid dynamics research centers on efficiently and accurately modeling fluid flows where density remains constant. Current efforts heavily utilize machine learning, particularly neural networks (including physics-informed neural networks and graph neural networks), often integrated with traditional methods like finite element or finite difference schemes, to solve the governing equations (e.g., Navier-Stokes) and related inverse problems. This focus stems from the high computational cost of traditional numerical simulations, especially for complex geometries and multi-parameter scenarios. Improved accuracy, efficiency, and generalizability of these models hold significant promise for advancing scientific understanding and practical applications in diverse fields, such as environmental modeling and engineering design.