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arXiv · 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
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Abstract

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.

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Chulin Wang, Kyongmin Yeo, Xiao Jin, Andres Codas, Levente J. Klein, Bruce Elmegreen. 2021-11-08. S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process. https://arxiv.org/abs/2111.04639

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