KAN-SAE applies nonlinear per-feature B-spline activations in sparse autoencoders to discover 72% more alive climate features and interpretable patterns such as European heatwaves and Pacific typhoons in deep learning weather models.
Sonny: Breaking the compute wall in medium-range weather forecasting.arXiv preprint arXiv:2603.21284, 2026
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Beyond Linear Superposition: Discovering Climate Features in AI Weather Models with KAN-SAE
KAN-SAE applies nonlinear per-feature B-spline activations in sparse autoencoders to discover 72% more alive climate features and interpretable patterns such as European heatwaves and Pacific typhoons in deep learning weather models.