Geometric Kolmogorov--Arnold Network (GeoKAN)

• URL: http://arxiv.org/abs/2605.06740v1
• Date: Thu, 07 May 2026 14:24:53 GMT
• Title: Geometric Kolmogorov--Arnold Network (GeoKAN)
• Abstract summary: We introduce a family of geometry-aware KAN-type models in which approximation is carried out in learned, geometry-adapted coordinates.<n>Within this framework, we develop three main variants, namely GeoKAN-NNMetric, GeoKAN-$$, and LM-KAN.<n>These variants allow us to study geometry-aware KAN models both as general function approximators and as surrogates in physics-informed learning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Geometric Kolmogorov--Arnold Networks (GeoKANs), a family of geometry-aware KAN-type models in which approximation is carried out in learned, geometry-adapted coordinates rather than in fixed Euclidean input coordinates. GeoKAN achieves this by learning a diagonal Riemannian metric that warps the input before basis expansion and feature mixing. The learned metric provides a geometric inductive bias through local length scaling and volume distortion, and in physics-informed settings it also affects the differential structure seen by the model. Within this framework, we develop three main variants, namely GeoKAN-NNMetric, GeoKAN-$γ$, and LM-KAN. For LM-KAN, we further consider three basis-specific versions, LM-KAN-RBF, LM-KAN-Wav, and LM-KAN-Fourier. These variants allow us to study geometry-aware KAN models both as general function approximators and as surrogates in physics-informed learning. By stretching regions with rapid variation and compressing smoother regions, GeoKAN reallocates representational resolution in a task-dependent manner, allowing the model to place capacity where it is most needed. As a result, GeoKAN is well suited to sharp, stiff, localized, and strongly non-uniform regimes arising in scientific machine learning and differential-equation problems.

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