发表机构
CMU; DP Tech（卡内基梅隆大学; 德璞科技）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Fluid-DiT是一种无图扩散Transformer，以注意力去噪替代图消息传递，通过隐空间公式解耦几何保真度与分布学习，在流体流动模拟的基准测试中优于图扩散基线，且泛化性与可扩展性强。
AI 中文摘要
模拟复杂流体流动需要捕捉完整的平衡分布而非仅平均轨迹，但高保真求解器的计算成本仍过高。近期进展如扩散图网络（DGNs）将扩散模型与图神经网络结合，可从非结构化网格直接采样平衡态，即便从短模拟也能实现分布精度。然而，基于图的扩散方法存在手工架构约束、消息传递感受野有限、多尺度设计成本高的问题，限制了其向更大更复杂领域的可扩展性。我们提出Fluid-DiT，一种无图扩散Transformer，用基于注意力的去噪替代图消息传递，消除显式图设计的同时保留了对混沌流动分布的建模能力。我们的框架引入了隐空间公式，将几何保真度与分布学习解耦，减少高频伪影并加速采样。通过利用Transformer的全局感受野，Fluid-DiT无需分层图粗化即可自然捕捉局部流动结构和长程相关性。在层流圆柱尾流、椭圆流系统、三维机翼湍流实验等经典基准测试中，Fluid-DiT在样本质量和分布精度上始终优于基于图的扩散基线，取得更高的R²相关性和更低的Wasserstein距离。此外，它能从短的、不完整的轨迹稳健泛化到未见的雷诺数和几何结构，展现出强大的可扩展性。
英文摘要
Simulating complex fluid flows requires capturing full equilibrium distributions rather than just mean trajectories, yet high-fidelity solvers remain computationally prohibitive. Recent advances, such as Diffusion Graph Networks (DGNs), have combined diffusion models with graph neural networks to sample equilibrium states directly from unstructured meshes, enabling distributional accuracy even from short simulations. However, graph-based diffusion approaches suffer from hand-crafted architectural constraints, limited receptive fields in message passing, and costly multi-scale designs, which restrict scalability to larger and more complex domains. We propose Fluid-DiT, a Graph-Free Diffusion Transformer that replaces graph message passing with attention-based denoising, eliminating explicit graph design while preserving the ability to model distributions of chaotic flows. Our framework introduces a latent-space formulation that disentangles geometric fidelity from distributional learning, reducing high-frequency artifacts and accelerating sampling. By leveraging the transformer's global receptive field, Fluid-DiT naturally captures both local flow structures and long-range correlations without requiring hierarchical graph coarsening. On canonical benchmarks including laminar cylinder wakes, ellipse-flow systems, and turbulent 3D wing experiments, Fluid-DiT consistently outperforms graph-based diffusion baselines in both sample quality and distributional accuracy, achieving higher $R^2$ correlations and lower Wasserstein distances. Moreover, it generalizes robustly from short, incomplete trajectories to unseen Reynolds numbers and geometries, demonstrating strong scalability.
