Fluid motion can be considered as point cloud transformation when adopted by
a Lagrangian description. Compared to traditional numerical analysis methods,
using machine learning techniques to learn physics simulations can achieve near
accuracy, 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 dual-pipeline architecture, seamlessly
integrated with an Attention-based Feature Fusion Module. Unlike previous
single-pipeline approaches, we find that a well-designed dual-pipeline approach
achieves a better balance between global fluid control and physical law
constraints. Furthermore, 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. The experiments
show that our approach significantly increases the accuracy of fluid simulation
predictions and enhances generalizability to previously unseen scenarios. We
demonstrate its superior performance over the state-of-the-art approaches
across various metrics.

提出一种创新的基于注意力的双通道网络方法，通过使用双通道架构和注意力特征融合模块，实现对三维流体模拟的精确预测和流体 - 固体耦合问题的优化。