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arXiv 2407.00735physics.flu-dyncs.LG
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利用扩散模型生成障碍物周围的流场预测

Generative prediction of flow fields around an obstacle using the diffusion model

State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University, Beijing 100871, China(湍流与复杂系统国家重点实验室,工程学院,北京大学,北京100871,中国);HEDPS-CAPT, Peking University, Beijing 100871, China(HEDPS-CAPT,北京大学,北京100871,中国)

Jiajun Hu, Zhen Lu, Yue Yang

AI总结:

本文提出了一种基于扩散模型的几何到流场预测方法,通过U-Net和交叉注意力机制实现对障碍物周围流场的高效预测,并在复杂几何条件下展现优越性能。

摘要:

我们提出了一种几何到流场的扩散模型,利用障碍物形状作为输入来预测障碍物周围的流场。该模型基于可学习的马尔可夫转移核,从高斯分布恢复数据分布。马尔可夫过程受障碍物几何形状的条件限制,估计每一步需要移除的噪声,通过U-Net实现。交叉注意力机制将几何形状作为提示。我们使用包含圆形、椭圆、矩形和三角形的简单障碍物流场数据集来训练几何到流场的扩散模型。为了比较,还训练了两个基于CNN的模型和一个VAE模型。在简单和复杂几何形状的障碍物周围的流场上进行测试,分别代表几何条件的插值和泛化。为了评估在严苛条件下的性能,测试集包含交叉和字符`PKU'等场景。生成的流场显示,几何到流场的扩散模型在预测瞬时流场和处理复杂几何形状方面优于基于CNN的模型和VAE模型。准确性与发散性的定量分析证明了模型的鲁棒性。

英文摘要:

We propose a geometry-to-flow diffusion model that utilizes obstacle shape as input to predict a flow field around an obstacle. The model is based on a learnable Markov transition kernel to recover the data distribution from the Gaussian distribution. The Markov process is conditioned on the obstacle geometry, estimating the noise to be removed at each step, implemented via a U-Net. A cross-attention mechanism incorporates the geometry as a prompt. We train the geometry-to-flow diffusion model using a dataset of flows around simple obstacles, including circles, ellipses, rectangles, and triangles. For comparison, two CNN-based models and a VAE model are trained on the same dataset. Tests are carried out on flows around obstacles with simple and complex geometries, representing interpolation and generalization on the geometry condition, respectively. To evaluate performance under demanding conditions, the test set incorporates scenarios including crosses and the characters `PKU.' Generated flow fields show that the geometry-to-flow diffusion model is superior to the CNN-based models and the VAE model in predicting instantaneous flow fields and handling complex geometries. Quantitative analysis of the accuracy and divergence demonstrates the model's robustness.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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