Physically constrained generative adversarial networks for improving precipitation fields from Earth system models. - Philipp Hess; Markus Drüke; Stefan Petri; Felix M. Strnad; Niklas Boers (Nat. Mac. Intell., 2022)

Deep Learning for bias-correcting comprehensive high-resolution Earth system models. - Philipp Hess; Stefan Lange; Niklas Boers (CoRR, 2023)

Fast, Scale-Adaptive, and Uncertainty-Aware Downscaling of Earth System Model Fields with Generative Foundation Models. - Philipp Hess; Michael Aich; Baoxiang Pan; Niklas Boers (CoRR, 2024)

Conditional diffusion models for downscaling & bias correction of Earth system model precipitation. - Michael Aich; Philipp Hess; Baoxiang Pan; Sebastian Bathiany; Yu Huang; Niklas Boers (CoRR, 2024)

Fast, scale-adaptive and uncertainty-aware downscaling of Earth system model fields with generative machine learning. - Philipp Hess; Michael Aich; Baoxiang Pan; Niklas Boers (Nat. Mac. Intell., 2025)

Diffusion models for probabilistic precipitation generation from atmospheric variables. - Michael Aich; Sebastian Bathiany; Philipp Hess; Yu Huang; Niklas Boers (CoRR, 2025)

Generating time-consistent dynamics with discriminator-guided image diffusion models. - Philipp Hess; Maximilian Gelbrecht; Christof Schötz; Michael Aich; Yu Huang; Shangshang Yang; Niklas Boers (CoRR, 2025)

Physics-constrained generative machine learning-based high-resolution downscaling of Greenland's surface mass balance and surface temperature. - Nils Bochow; Philipp Hess; Alexander Robinson (CoRR, 2025)