SOURCE-LINKED INTELLIGENCE
Dandelion: A Spherical Flower for Neural Simulation of Planetary Dynamics
Many dynamical processes unfold on the sphere but the default scientific machine learning architectures are Euclidean. Applying these architectures on a regular lat-lon grid causes problems: Cartesian convolutions become distorted at high latitude; 2D FFTs in Fourier neural operators incorrectly assume double periodicity; Cartesian positional encodings in ViTs distort spherical geodesic distances. Recent work moves towards natively spherical primitives, including spherical convolutions (e.g., DeepSphere or DISCO), Spherical Fourier Neural Operators (SFNOs), and geodesic attention. Here we prop
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T12:43:03.000Z
First collected: 2026-09-21T08:32:02.028Z. This is not the publication date.