Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling. - Tom Beucler; Stephan Rasp 001; Michael S. Pritchard; Pierre Gentine (CoRR, 2019)

Generative Modeling of Atmospheric Convection. - Griffin Mooers; Jens Tuyls; Stephan Mandt; Mike Pritchard; Tom G. Beucler (CI, 2020)

Towards Physically-Consistent, Data-Driven Models of Convection. - Tom Beucler; Michael S. Pritchard; Pierre Gentine; Stephan Rasp 001 (IGARSS, 2020)

Generative Modeling for Atmospheric Convection. - Griffin Mooers; Jens Tuyls; Stephan Mandt; Michael S. Pritchard; Tom Beucler (CoRR, 2020)

Analyzing High-Resolution Clouds and Convection using Multi-Channel VAEs. - Harshini Mangipudi; Griffin Mooers; Mike Pritchard; Tom Beucler; Stephan Mandt (CoRR, 2021)

Climate-Invariant Machine Learning. - Tom Beucler; Michael S. Pritchard; Janni Yuval; Ankitesh Gupta; Liran Peng; Stephan Rasp 001; Fiaz Ahmed; Paul A. O'Gorman; J. David Neelin; Nicholas J. Lutsko; Pierre Gentine (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)

Physics-constrained deep learning postprocessing of temperature and humidity. - Francesco Zanetta; Daniele Nerini; Tom Beucler; Mark A. Liniger (CoRR, 2022)

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)

Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery. - Saranya Ganesh S.; Tom Beucler; Frederick Iat-Hin Tam; Milton S. Gomez; Jakob Runge; Andreas Gerhardus (CoRR, 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)

Systematic Sampling and Validation of Machine Learning-Parameterizations in Climate Models. - Jerry Lin; Sungduk Yu; Tom Beucler; Pierre Gentine; David Walling; Mike Pritchard (CoRR, 2023)

Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications. - Tom Beucler; Erwan Koch; Sven Kotlarski; David Leutwyler; Adrien Michel; Jonathan Koh (CoRR, 2023)

Lessons Learned: Reproducibility, Replicability, and When to Stop. - Milton S. Gomez; Tom Beucler (CoRR, 2024)

Identifying Three-Dimensional Radiative Patterns Associated with Early Tropical Cyclone Intensification. - Frederick Iat-Hin Tam; Tom Beucler; James H. Ruppert Jr. (CoRR, 2024)

Lightning-Fast Thunderstorm Warnings: Predicting Severe Convective Environments with Global Neural Weather Models. - Monika Feldmann; Tom Beucler; Milton Gomez; Olivia Martius (CoRR, 2024)

Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applications. - Tom Beucler; Arthur Grundner; Sara Shamekh; Peter Ukkonen; Matthew Chantry; Ryan Lagerquist (CoRR, 2024)

Improving Predictions of Convective Storm Wind Gusts through Statistical Post-Processing of Neural Weather Models. - Antoine Leclerc; Erwan Koch; Monika Feldmann; Daniele Nerini; Tom Beucler (CoRR, 2025)

Investigating the Robustness of Extreme Precipitation Super-Resolution Across Climates. - Louise Largeau; Erwan Koch; David Leutwyler; Grégoire Mariéthoz; Valérie Chavez-Demoulin; Tom Beucler (CoRR, 2025)