用连续卷积与注意力融合，提升粒子流体模拟的稳定性
FluidFormer: Transformer with Continuous Convolution for Particle-based Fluid Simulation
- 局部用连续卷积，全局用自注意力，双路径协同建模
- 在复杂流体场景中显著减少误差累积，提升模拟稳定性
- 首个专为连续流体设计的Transformer架构，适合高精度仿真
基于学习的流体模拟网络已被证明是传统纳维-斯托克斯方程数值求解器的可行替代方案。现有神经方法遵循光滑粒子流体动力学（SPH）框架，仅依赖粒子间的局部相互作用。然而，我们强调全局上下文融合对学习型方法稳定复杂流体模拟同样重要。为此，提出首个具有局部-全局层次结构的流体注意力模块（FAB），其中连续卷积提取局部特征，自注意力捕获全局依赖。该融合机制有效抑制误差累积，建模长程物理现象。此外，首次构建专为连续流体模拟设计的Transformer架构，并无缝集成于双管道架构中。FluidFormer通过统一卷积式局部特征与注意力式全局上下文建模，建立神经流体模拟新范式。实验表明，该方法在复杂流体场景中表现优于现有方法，具备更强稳定性。
原文摘要 · Abstract (English)
Learning-based fluid simulation networks have been proven as viable alternatives to traditional numerical solvers for the Navier-Stokes equations. Existing neural methods follow Smoothed Particle Hydrodynamics (SPH) frameworks, which inherently rely only on local inter-particle interactions. However, we emphasize that global context integration is also essential for learning-based methods to stabilize complex fluid simulations. We propose the first Fluid Attention Block (FAB) with a local-global hierarchy, where continuous convolutions extract local features while self-attention captures global dependencies. This fusion suppresses the error accumulation and models long-range physical phenomena. Furthermore, we pioneer the first Transformer architecture specifically designed for continuous fluid simulation, seamlessly integrated within a dual-pipeline architecture. Our method establishes a new paradigm for neural fluid simulation by unifying convolution-based local features with attention-based global context modeling. FluidFormer demonstrates state-of-the-art performance, with stronger stability in complex fluid scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。