8 citations · 16 across the 13 of their papers we have counts for
5 papers · 1 filter
NeuralCrop: Combining physics and machine learning for improved crop yield projections
Yunan Lin, Sebastian Bathiany, Maha Badri +6
Global gridded crop models (GGCMs) are crucial to project the impacts of climate change on agricultural productivity and assess associated risks for food security. Despite decades…
Machine-Precision Prediction of Low-Dimensional Chaotic Systems from Noise-Free Data
Christof Schötz, Niklas Boers
Low-dimensional chaotic systems such as the Lorenz-63 model are commonly used to benchmark system-agnostic methods for learning dynamics from data. This study shows that learning f…
Generating time-consistent dynamics with discriminator-guided image diffusion models
Philipp Hess, Maximilian Gelbrecht, Christof Schötz +4
Realistic temporal dynamics are crucial for many video generation, processing and modelling applications, e.g. in computational fluid dynamics, weather prediction, or long-term cli…
Generating realistic global precipitation fields from modelled atmospheric circulation
Michael Aich, Sebastian Bathiany, Philipp Hess +2
Improving the representation of precipitation in Earth system models (ESMs) is critical for assessing the impacts of climate change and especially of extreme events like floods and…
Generative assimilation and prediction for weather and climate
Shangshang Yang, Congyi Nai, Xinyan Liu +11
Machine learning models have shown great success in predicting weather up to two weeks ahead, outperforming process-based benchmarks. However, existing approaches mostly focus on t…