Diffusion Models

Background Global climate datasets (reanalyses or CMIP simulations) are commonly available as monthly means and daily aggregates (mean, max., and min.) for key atmospheric variables. In this study, we compiled such temporal low-resolution data from the ERA5 [1] reanalysis, using high-resolution hourly data from the CERRA [2] regional reanalysis as ground truth Objectives We address the challenge of temporal downscaling (generate high-resolutuion climate predictions) by fine-tuning foundational AI weather models, such as GenCast [3], to transform coarse monthly/daily climate inputs into high-resolution hourly fields. This approach enables a physically consistent reconstruction of the diurnal cycle and peak intensity of heatwaves over Denmark. ...

Background The All-atom Diffusion Transformer (ADiT) represents a significant advancement in the generative modeling of 3D atomic systems. Unlike traditional models that are tailored specifically for either molecules or materials, ADiT introduces a unified latent diffusion framework capable of jointly generating both periodic materials and non-periodic molecular systems using a single model. This is achieved through a two-stage process: An autoencoder maps unified, all-atom representations of molecules and materials to a shared latent embedding space. ...