A Denoising Diffusion Model for Fluid Field Prediction

Understand

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.