几何科拉莫戈罗夫-阿诺尔德网络（GeoKAN）
Geometric Kolmogorov--Arnold Network (GeoKAN)
AI总结：
GeoKAN通过学习几何适应坐标进行函数逼近，提升科学计算和微分方程问题的建模能力。
摘要：
GeoKAN通过学习几何适应的坐标进行函数逼近，提升科学计算和微分方程问题的建模能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
