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 dual-pipeline 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 dual-pipeline 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.

翻译：流体运动在使用SPH方法时可视为点云变换。相较于传统数值分析方法，采用机器学习技术学习物理仿真可在保持高精度的同时显著提升计算效率。本文提出一种基于注意力机制双管道网络的创新三维流体仿真方法，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。不同于以往需要在全局流体控制与物理规律约束之间艰难权衡的方法，我们通过精心设计的双管道方案实现了这两个关键要素的更好平衡。此外，我们设计了类型感知输入模块，该模块可自适应识别不同类型的粒子并执行后续特征融合，从而更好地处理流固耦合问题。为进一步探究网络处理复杂场景的能力，我们构建了新型数据集Tank3D。实验表明，本方法不仅在各评估指标上实现定量提升并超越现有最优方法，更通过严格遵循物理定律在基于神经网络的仿真领域实现了质的飞跃。代码与视频演示详见https://github.com/chenyu-xjtu/DualFluidNet。