We propose a novel denoising diffusion generative model for predicting nonlinear fluid fields named FluidDiff. By performing a diffusion process, the model is able to learn a complex representation of the high-dimensional dynamic system, and then Langevin sampling is used to generate predictions for the flow state under specified initial conditions. The model is trained with finite, discrete fluid simulation data. We demonstrate that our model has the capacity to model the distribution of simulated training data and that it gives accurate predictions on the test data. Without encoded prior knowledge of the underlying physical system, it shares competitive performance with other deep learning models for fluid prediction, which is promising for investigation on new computational fluid dynamics methods.

翻译：我们提出了一种名为FluidDiff的新型降噪扩散生成模型，用于预测非线性流体场。通过执行扩散过程，该模型能够学习高维动态系统的复杂表示，随后利用朗之万采样在指定初始条件下生成流态的预测。该模型使用有限的离散流体仿真数据进行训练。我们证明了该模型具备模拟仿真训练数据分布的能力，并对测试数据给出了准确的预测。无需编码底层物理系统的先验知识，该模型在流体预测方面与其它深度学习模型相比展现出具有竞争力的性能，这为研究新型计算流体力学方法提供了有前景的方向。