DualFluidNet: An attention-based dual-pipeline network for fluid simulation

Fluid motion can be considered as a point cloud transformation when using the SPH method. Compared to traditional numerical analysis methods, using machine learning techniques to learn physics simulations can achieve near -accurate results, while significantly increasing efficiency. In this paper, we propose an innovative approach for 3D fluid simulations utilizing an Attention -based Dual -pipeline Network, which employs a dualpipeline architecture, seamlessly integrated with an Attention -based Feature Fusion Module. Unlike previous methods, which often make difficult trade-offs between global fluid control and physical law constraints, we find a way to achieve a better balance between these two crucial aspects with a well -designed dualpipeline approach. Additionally, we design a Type -aware Input Module to adaptively recognize particles of different types and perform feature fusion afterward, such that fluid -solid coupling issues can be better dealt with. Furthermore, we propose a new dataset, Tank3D, to further explore the network's ability to handle more complicated scenes. The experiments demonstrate that our approach not only attains a quantitative enhancement in various metrics, surpassing the state-of-the-art methods, but also signifies a qualitative leap in neural network -based simulation by faithfully adhering to the physical laws. Code and video demonstrations are available at https://github.com/chenyu-xjtu/DualFluidNet.