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The paper proposes Neural Tangent Kernel-based uncertainty quantification for deterministic deep learning weather models, achieving sharper adaptive prediction intervals during extreme events without retraining.
This paper develops a local theory of gradient descent near bifurcations in dynamical models, showing that the state-space neural tangent kernel collapses to a rank-one operator that dominates learning dynamics, making optimization effectively low-dimensional and predictable from normal forms.