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.
@article{arxiv.2111.04639,
title = {S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process},
author = {Chulin Wang and Kyongmin Yeo and Xiao Jin and Andres Codas and Levente J. Klein and Bruce Elmegreen},
journal= {arXiv preprint arXiv:2111.04639},
year = {2023}
}