A Denoising Diffusion Model for Fluid Field Prediction
This work addresses fluid prediction for computational fluid dynamics, but it is incremental as it applies an existing diffusion method to a new domain.
The authors tackled the problem of predicting nonlinear fluid fields by proposing FluidDiff, a denoising diffusion generative model, which achieved competitive performance with other deep learning models on test data without prior physical knowledge.
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.