Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
Authors: Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, Anima Anandkumar
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply SFNOs to forecasting atmospheric dynamics, and demonstrate stable autoregressive rollouts for a year of simulated time (1,460 steps), while retaining physically plausible dynamics. The SFNO has important implications for machine learning-based simulation of climate dynamics that could eventually help accelerate our response to climate change. |
| Researcher Affiliation | Collaboration | 1NVIDIA Corp., Santa Clara, USA 2Caltech, Pasadena, USA. |
| Pseudocode | No | No explicit pseudocode or algorithm blocks are provided in the paper. |
| Open Source Code | Yes | torch-harmonics and our implementation of the SFNO are available to the public at https://github.com/ NVIDIA/torch-harmonics |
| Open Datasets | Yes | The proposed method is applied to the Earth Reanalysis 5 dataset (ERA5) (Hersbach et al., 2020), one of the best estimates of Earth s historical weather and climate over the period 1950-present |
| Dataset Splits | Yes | We use 40 years of ERA5 (1979-2018): 1979-2015 is used for training, 2016 and 2017 are used for validation, hyperparameter tuning, and model selection, and 2018 is held out as out-of-sample test set. |
| Hardware Specification | Yes | As each autoregressive step takes around 500ms on a NVIDIA A6000 GPU |
| Software Dependencies | Yes | To enable our method, we implement torch-harmonics, a library for differentiable Spherical Harmonics written in Py Torch (Paszke et al., 2019). |
| Experiment Setup | Yes | Models are trained with the following hyperparameters: 4 (S)FNO blocks, a down-scaling factor of 3, and embedding dimensions of 256. |