  Super-Resolution (SR) techniques aim to enhance data resolution, enabling the
retrieval of finer details, and improving the overall quality and fidelity of
the data representation. There is growing interest in applying SR methods to
complex spatiotemporal systems within the Scientific Machine Learning (SciML)
community, with the hope of accelerating numerical simulations and/or improving
forecasts in weather, climate, and related areas. However, the lack of
standardized benchmark datasets for comparing and validating SR methods hinders
progress and adoption in SciML. To address this, we introduce SuperBench, the
first benchmark dataset featuring high-resolution datasets (up to
$2048\times2048$ dimensions), including data from fluid flows, cosmology, and
weather. Here, we focus on validating spatial SR performance from data-centric
and physics-preserved perspectives, as well as assessing robustness to data
degradation tasks. While deep learning-based SR methods (developed in the
computer vision community) excel on certain tasks, despite relatively limited
prior physics information, we identify limitations of these methods in
accurately capturing intricate fine-scale features and preserving fundamental
physical properties and constraints in scientific data. These shortcomings
highlight the importance and subtlety of incorporating domain knowledge into ML
models. We anticipate that SuperBench will significantly advance SR methods for
scientific tasks.
