A library for differentiable signal processing and machine learning on the sphere
cs.LG, physics.ao-ph, physics.geo-ph
Submitted: 2026-09-30
Updated: 2026-09-30
Sources
- Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
- FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale
- Attention on the Sphere
- Does equivariance matter at scale?
- Efficient Generalized Spherical CNNs
- Group Equivariant Convolutional Networks
- Spherical CNNs
- DeepSphere: a graph-based spherical CNN
- Spin-Weighted Spherical CNNs
- Scaling Spherical CNNs
- LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensembles
- HealDA: Highlighting the importance of initial errors in end-to-end AI weather forecasts
- Neural General Circulation Models for Weather and Climate
- AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score
- MARA: Continuous SE(3)-Equivariant Attention for Molecular Force Fields
- Fourier Neural Operator for Parametric Partial Differential Equations
- Neural Operators with Localized Integral and Differential Kernels
- Toward Data-Driven Surrogates of the Solar Wind with Spherical Fourier Neural Operator
- Physics-Aware Neural Operators for Direct Inversion in 3D Photoacoustic Tomography
- ACE: A fast, skillful learned global atmospheric model for climate prediction
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