Supervising the Path to Fine Scales: GalerkinFlow for Scientific-Field and Image Super-Resolution
Most super-resolution models learn from paired data by supervising only the final high-resolution output. This provides little control over how the prediction should evolve between the downsampled observation and its fine target. We introduce GalerkinFlow, an equation-agnostic framework that turns each coarse--fine pai...