8 citations · 16 across the 13 of their papers we have counts for
17 papers
Second-Order Flows for Approaching Stationary Points via the SAV-E Approach
Haifan Chen, Wei Liu, Ziqing Xie +1
Second-order flows represent an emerging computational framework for approaching stationary points of variational energies, characterized by damped hyperbolic partial differential…
ClimTip-GML: A global bias-corrected and downscaled dataset for assessing impacts of climate tipping events
Philipp Hess, Sebastian Bathiany, Lucas Ferreira Correa +3
Assessing the impacts of future climate scenarios including tipping events of major Earth system components such as the Amazon rainforest (ARF) or the Atlantic meridional overturni…
Flexible generation of daily Earth system model projections across radiative forcing scenarios
Yu Huang, Sebastian Bathiany, Shangshang Yang +3
Earth system model (ESM) projections of the climate system's response to anthropogenic forcing are central to assess the impacts of climate change and inform adaptation and mitigat…
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…
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…
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…
A Generative Machine Learning Approach for Improving Precipitation from Earth System Models
Philipp Hess, Niklas Boers
Quantifying the impacts of anthropogenic global warming requires accurate Earth system model (ESM) simulations. Statistical bias correction and downscaling can be applied to reduce…
Conditional diffusion models for downscaling and bias correction of Earth system model precipitation
Michael Aich, Philipp Hess, Baoxiang Pan +3
Climate change exacerbates extreme weather events like heavy rainfall and flooding. As these events cause severe socioeconomic damage, accurate high-resolution simulation of precip…
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…
Physically Constrained Generative Adversarial Networks for Improving Precipitation Fields from Earth System Models
Philipp Hess, Markus Drüke, Stefan Petri +2
Precipitation results from complex processes across many scales, making its accurate simulation in Earth system models (ESMs) challenging. Existing post-processing methods can impr…
Analysis of a bistable climate toy model with physics-based machine learning methods
Maximilian Gelbrecht, Valerio Lucarini, Niklas Boers +1
We propose a comprehensive framework able to address both the predictability of the first and of the second kind for high-dimensional chaotic models. For this purpose, we analyse t…