Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Reconstructing high-resolution (HR) information from a low-resolution (LR) data has been of great interest. While most of the so-called super-resolution (SR) models rely on a supervised training with high-resolution ground truth data, in many real-life problems, such ground truth data is either difficult to create or nonexistent. Here, we present a deep learning model for a space-time SR from a sequence of LR images for advection-diffusion problems without the ground truth HR data. We use a state-space representation to reconstruct the HR fields with the mass conservation constraints. The proposed method is verified by using two-dimensional CFD simulations.
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Natalia Martinez Gil, Dhaval Patel, et al.
UAI 2024
Shubhi Asthana, Pawan Chowdhary, et al.
KDD 2021
Pavithra Harsha, Ali Koc, et al.
INFORMS 2021