[50] Stephan Rasp, Peter D. Dueben, Sebastian Scher, Jonathan A. Weyn, SouKayna Mouatadid, and Nils Thuerey. WeatherBench: A benchmark data set for data-driven weather forecasting. Journal of Advances in Modeling Earth Systems, 2020. URL https://doi.org/10.1029/2F2020ms002203.
[51] Kashif Rasul, Calvin Seward, Ingmar Schuster, and Roland Vollgraf. Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting. In ICML, 2021.
[52] Suman Ravuri, Karel Lenc, Matthew Willson, Dmitry Kangin, Remi Lam, Piotr Mirowski, Megan Fitzsimons, Maria Athanassiadou, Sheleem Kashem, Sam Madge, et al. Skilful precipitation nowcasting using deep generative models of radar. Nature, 597(7878):672-677, 2021. URL https://doi.org/10.1038/s41586-021-03854-z.
[53] Severi Rissanen, Markus Heinonen, and Arno Solin. Generative modelling with inverse heat dissipation. In The Eleventh International Conference on Learning Representations, 2023.
[54] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In IEEE Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022.
[55] Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter W. Battaglia. Learning to simulate complex physics with graph networks. In International Conference on Machine Learning, 2020.
[56] S. Scher. Toward data-driven weather and climate forecasting: Approximating a simple general circulation model with deep learning. Geophysical Research Letters, 45(22): 12,616-12,622, 2018. doi: https://doi.org/10.1029/2018GL080704. URL https://agupubs. onlinelibrary.wiley.com/doi/abs/10.1029/2018GL080704.
[57] S. Scher and G. Messori. Generalization properties of feed-forward neural networks trained on lorenz systems. Nonlinear Processes in Geophysics, 26(4):381-399, 2019. doi: 10.5194/npg-26-381-2019. URL https://npg.copernicus.org/articles/26/381/2019/.
[58] Sebastian Scher and Gabriele Messori. Ensemble methods for neural network-based weather forecasts. Journal of Advances in Modeling Earth Systems, 13(2), 2021. doi: https://doi.org/10.1029/2020MS002331. URL https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020MS002331.
[59] Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, Devi Parikh, Sonal Gupta, and Yaniv Taigman. Make-a-video: Text-to-video generation without text-video data. 2022. URL https://arxiv.org/abs/2209.14792.
[60] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In Proceedings of the 32nd International Conference on Machine Learning, 2015. URL https://proceedings.mlr.press/v37/sohl-dickstein15.html.
[61] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In International Conference on Learning Representations, 2021.
[62] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. In Advances in Neural Information Processing Systems, volume 32, 2019.
[63] Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. In International Conference on Learning Representations, 2020.
[64] Jannik Thuemmel, Matthias Karlbauer, Sebastian Otte, Christiane Zarfl, Georg Martius, Nicole Ludwig, Thomas Scholten, Ulrich Friedrich, Volker Wulfmeyer, Bedartha Goswami, and Martin V. Butz. Inductive biases in deep learning models for weather prediction. 2023.
[65] Alasdair Tran, Alexander Mathews, Lexing Xie, and Cheng Soon Ong. Factorized fourier neural operators. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=tmIiMP14IPa.
[66] Renbo Tu, Nicholas Roberts, Vishak Prasad, Sibasis Nayak, Paarth Jain, Frederic Sala, Ganesh Ramakrishnan, Ameet Talwalkar, Willie Neiswanger, and Colin White. Automl for climate change: A call to action. 2022. URL https://arxiv.org/abs/2210.03324.