| Climate-invariant machine learning T Beucler, P Gentine, J Yuval, A Gupta, L Peng, J Lin, S Yu, S Rasp, ... Science Advances 10 (6), eadj7250, 2024 | 67 | 2024 |
| 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 | 28 | 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 |
| Systematic sampling and validation of machine Learning-Parameterizations in climate models J Lin, S Yu, T Beucler, P Gentine, D Walling, M Pritchard arXiv preprint arXiv:2309.16177, 2023 | 9 | 2023 |
| Sampling Hybrid Climate Simulation at Scale to Reliably Improve Machine Learning Parameterization J Lin, S Yu, L Peng, T Beucler, E Wong-Toi, Z Hu, P Gentine, M Geleta, ... Authorea Preprints, 2024 | 2 | 2024 |
| Stress-testing the coupled behavior of hybrid physics-machine learning climate simulations on an unseen, warmer climate J Lin, MA Bhouri, TG Beucler, S Yu, M Pritchard NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning, 2023 | 1 | 2023 |
| Confronting the offline vs. online skill dilemma via prognostic testing of neural network convection parameterizations at a computationally ambitious scale J Lin, MS Pritchard, S Yu, T Beucler, D Walling AGU Fall Meeting Abstracts 2022, NG21A-01, 2022 | | 2022 |
| Navigating the Noise: Bringing Clarity to ML Parameterization Design with O (100) Ensembles J Lin, S Yu, L Peng, T Beucler, E Wong-Toi, Z Hu, P Gentine, M Geleta, ... | | |
Tom BeuclerAssistant Professor, University of Lausanneכתובת אימייל מאומתת בדומיין unil.ch