用2D数据高效重建3D流场，降低实验与计算成本。
Reconstructing 3D Flow from 2D Data with Diffusion Transformer
- 基于扩散变压器，融合2D平面位置信息实现任意切片重建。
- 采用窗口与平面注意力机制，计算量减少且精度不降。
- 适合流体模拟、实验测量等需要低成本3D重构的场景。
流体流动是广泛应用的物理问题，在多个领域至关重要。由于流体具有高度非线性和混沌特性，分析此类问题极为困难。计算流体动力学（CFD）虽为最佳分析工具，但3D仿真耗时且资源消耗大；实验中，粒子图像测速（PIV）成本随维度提升而增加。从2D PIV数据重建3D流场可降低成本并拓展应用。本文提出一种基于扩散变压器的方法，通过嵌入2D切片的位置信息，实现任意组合2D切片到3D流场的重建，增强灵活性。采用窗口注意力与平面注意力替代全局注意力，显著降低高维计算开销，同时保持性能。实验表明，该模型能高效准确地从2D数据重建出逼真的3D流场。
原文摘要 · Abstract (English)
Fluid flow is a widely applied physical problem, crucial in various fields. Due to the highly nonlinear and chaotic nature of fluids, analyzing fluid-related problems is exceptionally challenging. Computational fluid dynamics (CFD) is the best tool for this analysis but involves significant computational resources, especially for 3D simulations, which are slow and resource-intensive. In experimental fluid dynamics, PIV cost increases with dimensionality. Reconstructing 3D flow fields from 2D PIV data could reduce costs and expand application scenarios. Here, We propose a Diffusion Transformer-based method for reconstructing 3D flow fields from 2D flow data. By embedding the positional information of 2D planes into the model, we enable the reconstruction of 3D flow fields from any combination of 2D slices, enhancing flexibility. We replace global attention with window and plane attention to reduce computational costs associated with higher dimensions without compromising performance. Our experiments demonstrate that our model can efficiently and accurately reconstruct 3D flow fields from 2D data, producing realistic results.
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