| Non‐Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models G Behrens, T Beucler, P Gentine, F Iglesias‐Suarez, M Pritchard, V Eyring Journal of Advances in Modeling Earth Systems 14 (8), e2022MS003130, 2022 | 28 | 2022 |
| ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation S Yu, W Hannah, L Peng, J Lin, MA Bhouri, R Gupta, B Lütjens, JC Will, ... Advances in Neural Information Processing Systems 36, 22070-22084, 2023 | 25 | 2023 |
| ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators S Yu, WM Hannah, L Peng, MA Bhouri, R Gupta, J Lin, B Lütjens, JC Will, ... NeurIPS, 2023 | 13 | 2023 |
| Improving Atmospheric Processes in Earth System Models with Deep Learning Ensembles and Stochastic Parameterizations G Behrens, T Beucler, F Iglesias-Suarez, S Yu, P Gentine, M Pritchard, ... arXiv preprint arXiv:2402.03079, 2024 | 6 | 2024 |
| Understanding and Modelling Convection with Machine Learning G Behrens Staats-und Universitätsbibliothek Bremen, 2024 | | 2024 |
| ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation S Akshay, S Yu, W Hannah, L Peng, J Lin, MA Bhouri, G Ritwik, B Lütjens, ... | | 2024 |
| Simulating Atmospheric Processes in ESMs and Quantifying Uncertainties with Deep Learning Multi-Member and Stochastic Parameterizations G Behrens, T Beucler, F Iglesias-Suarez, S Yu, P Gentine, M Pritchard, ... | | 2024 |
| Physics to machine learning, and machine learning back to physics P Gentine, S Shamekh, A Anderson-Connolly, T Beucler, V Eyring, ... AGU Fall Meeting Abstracts 2022, NG15A-05, 2022 | | 2022 |
| Non-Linear Dimensionality Reduction with a Variational Autoencoder Decoder to Understand Convective Processes in Climate Models G Behrens, T Beucler, P Gentine, F Iglesias-Suarez, M Pritchard, V Eyring arXiv e-prints, arXiv: 2204.08708, 2022 | | 2022 |
| Non-Linear Dimensionality Reduction With a Variational Encoder Decoder (VED) to Understand Convective Processes in Climate Models G Behrens, T Beucler, P Gentine, F Iglesias-Suarez, M Pritchard, V Eyring | | 2022 |
| Machine learning-based parametrizations for the ICON model F Iglesias-Suarez, A Grundner, G Behrens, T Beucler, P Gentine, G Marco, ... AGU Fall Meeting Abstracts 2021, A14C-04, 2021 | | 2021 |
| Deep Learning based cloud parametrization for the Community Atmosphere Model G Behrens, V Eyring, P Gentine, MS Pritchard, T Beucler, S Rasp EGU2020, 2020 | | 2020 |
| Machine learning based cloud parametrizations and causal discovery for climate models V Eyring, P Gentine, G Behrens, MS Pritchard, S Rasp, J Runge AGU Fall Meeting Abstracts 2019, U34B-06, 2019 | | 2019 |
| Seasonal predictability in the MPI-ESM-LR using Modini initialization G Behrens Christian-Albrechts-Universität Kiel, 2018 | | 2018 |
| ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation A Subramaniam, S Yu, Z Hu, WM Hannah, L Peng, J Lin, MA Bhouri, ... AGU24, 0 | | |
