8 citations · 16 across the 13 of their papers we have counts for
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WIND: Weather Inverse Diffusion for Zero-Shot Atmospheric Modeling
Michael Aich, Andreas Fürst, Florian Sestak +3
Deep learning has revolutionized weather forecasting, but many challenges remain, including climate modeling. Moreover, the current landscape remains fragmented: highly specialized…
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…
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…
Improving the Noise Estimation of Latent Neural Stochastic Differential Equations
Linus Heck, Maximilian Gelbrecht, Michael T. Schaub +1
Latent neural stochastic differential equations (SDEs) have recently emerged as a promising approach for learning generative models from stochastic time series data. However, they…
Projected Neural Differential Equations for Learning Constrained Dynamics
Alistair White, Anna Büttner, Maximilian Gelbrecht +4
Neural differential equations offer a powerful approach for learning dynamical systems from data. However, they do not inherently respect known constraints, such as conservation la…
Machine Learning for Predicting Chaotic Systems
Christof Schötz, Alistair White, Maximilian Gelbrecht +1
Predicting chaotic dynamical systems is critical in many scientific fields, such as weather forecasting, but challenging due to the characteristic sensitive dependence on initial c…
Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints
Alistair White, Niki Kilbertus, Maximilian Gelbrecht +1
Many successful methods to learn dynamical systems from data have recently been introduced. However, ensuring that the inferred dynamics preserve known constraints, such as conserv…