SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning

Understand

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, including data from fluid flows, cosmology, and weather.