A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction
This addresses a limitation in machine learning models for fluid dynamics by reducing dependency on low-fidelity training data, which is incremental but improves application robustness.
The paper tackles the problem of high-fidelity flow field reconstruction in fluid dynamics by proposing a diffusion model that only uses high-fidelity data for training, enabling accurate reconstruction from low-fidelity or sparse inputs without retraining.
Machine learning models are gaining increasing popularity in the domain of fluid dynamics for their potential to accelerate the production of high-fidelity computational fluid dynamics data. However, many recently proposed machine learning models for high-fidelity data reconstruction require low-fidelity data for model training. Such requirement restrains the application performance of these models, since their data reconstruction accuracy would drop significantly if the low-fidelity input data used in model test has a large deviation from the training data. To overcome this restraint, we propose a diffusion model which only uses high-fidelity data at training. With different configurations, our model is able to reconstruct high-fidelity data from either a regular low-fidelity sample or a sparsely measured sample, and is also able to gain an accuracy increase by using physics-informed conditioning information from a known partial differential equation when that is available. Experimental results demonstrate that our model can produce accurate reconstruction results for 2d turbulent flows based on different input sources without retraining.