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的新型去噪扩散生成模型，用于预测非线性流场。通过执行扩散过程，该模型能够学习高维动力系统的复杂表示，并利用朗之万采样在指定初始条件下生成流场状态的预测。模型使用有限、离散的流体模拟数据进行训练。我们证明了该模型具有模拟训练数据分布的能力，并在测试数据上给出了准确的预测。在未编码底层物理系统先验知识的情况下，该模型与其他用于流场预测的深度学习模型具有竞争性表现，这为探索新的计算流体力学方法提供了前景。