Paper ID: 2111.04639

S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process

Chulin Wang, Kyongmin Yeo, Xiao Jin, Andres Codas, Levente J. Klein, Bruce Elmegreen

We present a super-resolution model for an advection-diffusion process with limited information. While most of the super-resolution models assume high-resolution (HR) ground-truth data in the training, in many cases such HR dataset is not readily accessible. Here, we show that a Recurrent Convolutional Network trained with physics-based regularizations is able to reconstruct the HR information without having the HR ground-truth data. Moreover, considering the ill-posed nature of a super-resolution problem, we employ the Recurrent Wasserstein Autoencoder to model the uncertainty.

Submitted: Nov 8, 2021