Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels

Understand

High-resolution (HR) information of fluid flows, although preferable, is usually less accessible due to limited computational or experimental resources.

• In many cases, fluid data are generally sparse, incomplete, and possibly noisy.
• How to enhance spatial resolution and decrease the noise level of flow data is essential and practically useful.
• Deep learning (DL) techniques have been demonstrated to be effective for super-resolution (SR) tasks, which, however, primarily rely on sufficient HR labels for training.