10 papers
Decision-Aware Training for Sample-Based Generative Models
Kornelius Raeth, Nicole Ludwig
Sample-based generative models are increasingly used for probabilistic forecasting in high-stakes decision settings, yet their training objectives are blind to the decision maker's…
Bounded Graph Clustering with Graph Neural Networks
Kibidi Neocosmos, Diego Baptista, Nicole Ludwig
In community detection, many methods require the user to specify the number of clusters in advance since an exhaustive search over all possible values is computationally infeasible…
Evaluating Weather Forecasts from a Decision Maker's Perspective
Kornelius Raeth, Nicole Ludwig
Standard weather forecast evaluations focus on the forecaster's perspective and on a statistical assessment comparing forecasts and observations. In practice, however, forecasts ar…
Fault Detection in Solar Thermal Systems using Probabilistic Reconstructions
Florian Ebmeier, Nicole Ludwig, Jannik Thuemmel +2
Solar thermal systems (STS) present a promising avenue for low-carbon heat generation, with a well-running system providing heat at minimal cost and carbon emissions. However, STS…
Assessing the risk of future Dunkelflaute events for Germany using generative deep learning
Felix Strnad, Jonathan Schmidt, Fabian Mockert +2
The European electricity power grid is transitioning towards renewable energy sources, characterized by an increasing share of off- and onshore wind and solar power. However, the w…
A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation
Jonathan Schmidt, Luca Schmidt, Felix Strnad +2
Local climate information is crucial for impact assessment and decision-making, yet coarse global climate simulations cannot capture small-scale phenomena. Current statistical down…