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DiffDA: a Diffusion model for weather-scale Data Assimilation
The experiments also show that the initial conditions assimilated from sparse observations (less than 0. 77% of gridded data) and 48-hour forecast can be used for forecast models with a loss of lead time of at most 24 hours compared to initial conditions from state-of-the-art data assimilation in ERA5.
Do Deep Learning Models Really Outperform Traditional Approaches in Molecular Docking?
What's more, they claim to perform better than traditional molecular docking, but the approach of comparison is not fair, since traditional methods are not designed for docking on the whole protein without a given pocket.
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