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.

翻译：流体运动在采用拉格朗日描述时可视为点云变换。与传统数值分析方法相比，利用机器学习技术学习物理模拟能在保持近似的精度的同时显著提升效率。本文提出了一种创新的三维流体模拟方法——基于注意力机制的双管道网络，该网络采用双管道架构并与注意力特征融合模块无缝集成。与以往单管道方法不同，我们发现精心设计的双管道方法能够在全局流体控制与物理定律约束之间实现更优平衡。此外，我们设计了类型感知输入模块，可自适应识别不同粒子类型并进行后续特征融合，从而更好地处理流固耦合问题。实验表明，本方法显著提升了流体模拟预测的精度，并增强了对未见场景的泛化能力。我们通过多种指标证明了其相较于现有最先进方法的优越性能。