27 citations · 55 across the 12 of their papers we have counts for
13 papers
Generative deep learning improves reconstruction of global historical climate records
Zhen Qian, Teng Liu, Sebastian Bathiany +7
Accurate assessment of anthropogenic climate change relies on historical instrumental data, yet observations from the early 20th century are sparse, fragmented, and uncertain. Conv…
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…
AI for a Planet Under Pressure
Victor Galaz, Maria Schewenius, Jonathan F. Donges +26
Artificial intelligence (AI) is already driving scientific breakthroughs in a variety of research fields, ranging from the life sciences to mathematics. This raises a critical ques…
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…
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…
Model adaptive phase space reconstruction
Jayesh M. Dhadphale, K. Hauke Kraemer, Maximilian Gelbrecht +3
Phase space reconstruction (PSR) methods allow for the analysis of low-dimensional data with methods from dynamical system theory, but their application to prediction models, like…
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…
Differentiable Programming for Earth System Modeling
Maximilian Gelbrecht, Alistair White, Sebastian Bathiany +1
Earth System Models (ESMs) are the primary tools for investigating future Earth system states at time scales from decades to centuries, especially in response to anthropogenic gree…
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…
Basin stability and limit cycles in a conceptual model for climate tipping cascades
Nico Wunderling, Maximilian Gelbrecht, Ricarda Winkelmann +2
Tipping elements in the climate system are large-scale subregions of the Earth that might possess threshold behavior under global warming with large potential impacts on human soci…
Monte Carlo Basin Bifurcation Analysis
Maximilian Gelbrecht, Jürgen Kurths, Frank Hellmann
Many high-dimensional complex systems exhibit an enormously complex landscape of possible asymptotic states. Here, we present a numerical approach geared towards analyzing such sys…