Nonlinear Causal Link Estimation Under Hidden Confounding with an Application to Time Series Anomaly Detection. - Violeta Teodora Trifunov; Maha Shadaydeh; Jakob Runge; Veronika Eyring; Markus Reichstein; Joachim Denzler (GCPR, 2019)

On the Generalization of Agricultural Drought Classification from Climate Data. - Julia Gottfriedsen; Max Berrendorf; Pierre Gentine; Markus Reichstein; Katja Weigel; Birgit Hassler; Veronika Eyring (CoRR, 2021)

Deep Learning Based Cloud Cover Parameterization for ICON. - Arthur Grundner; Tom Beucler; Fernando Iglesias-Suarez; Pierre Gentine; Marco A. Giorgetta; Veronika Eyring (CoRR, 2021)

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation. - Sungduk Yu; Walter M. Hannah; Liran Peng; Jerry Lin; Mohamed Aziz Bhouri; Ritwik Gupta; Björn Lütjens; Justus C. Will; Gunnar Behrens; Julius Busecke; Nora Loose; Charles Stern; et al. (NeurIPS, 2023)

ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators. - Sungduk Yu; Walter M. Hannah; Liran Peng; Mohamed Aziz Bhouri; Ritwik Gupta; Jerry Lin; Björn Lütjens; Justus C. Will; Tom Beucler; Bryce E. Harrop; Benjamin R. Hillman; Andrea M. Jenney; et al. (CoRR, 2023)

Transferring climate change knowledge. - Francesco Immorlano; Veronika Eyring; Thomas le Monnier de Gouville; Gabriele Accarino; Donatello Elia; Giovanni Aloisio; Pierre Gentine (CoRR, 2023)

Machine-Learned Cloud Classes From Satellite Data for Process-Oriented Climate Model Evaluation. - Arndt Kaps; Axel Lauer; Gustau Camps-Valls; Pierre Gentine; Luis Gómez-Chova; Veronika Eyring (IEEE Trans. Geosci. Remote. Sens., 2023)

Bootstrap aggregation and confidence measures to improve time series causal discovery. - Kevin Debeire; Andreas Gerhardus; Jakob Runge; Veronika Eyring (CLeaR, 2024)

Towards Physically Consistent Deep Learning For Climate Model Parameterizations. - Birgit Kühbacher; Fernando Iglesias-Suarez; Niki Kilbertus; Veronika Eyring (ICMLA, 2024)