Abstract: Fluid, as one of the most fundamental substances in nature, often requires a trade-off between accuracy and efficiency in its simulation. Thus, a novel end-to-end systematic convolutional network fluid simulator called PolarNet is proposed. Firstly, particle data is converted to a 3D polar coordinate representation, and the PolarConv spatial convolution structure is designed. Secondly, combined with a physical fluid simulator, a four-layer network structure is constructed to design the network fluid simulator PolarNet, achieving end-to-end fluid prediction. Additionally, physically-based constraints are carefully designed to enhance fluid incompressibility. Experimental results show that compared with traditional modeling-based simulators, PolarNet significantly improves the accuracy of fluid boundaries and maintains incompressibility while ensuring efficiency. Compared with the other learning-based fluid simulators, PolarNet maintains the highest prediction stability with fewer training parameters, benefiting from the compact representation of polar coordinates. PolarNet provides new methods and perspectives for multimodal information processing.

Key words:

fluid dynamics simulation , spatial convolutional network ,  data-driven ,  systematic simulator