Geometric Kolmogorov-Arnold Superposition Theorem

• URL: http://arxiv.org/abs/2502.16664v1
• Date: Sun, 23 Feb 2025 17:47:33 GMT
• Title: Geometric Kolmogorov-Arnold Superposition Theorem
• Authors: Francesco Alesiani, Takashi Maruyama, Henrik Christiansen, Viktor Zaverkin,
• Abstract summary: The Kolmogorov-Arnold Network (KAN) was introduced as a trainable model to implement the Kolmogorov-Arnold Theorem (KAT)<n>We propose a novel extension of KAT and KAN to incorporate equivariance and invariance over $O(n)$ group actions, enabling accurate and efficient modeling of physical systems.
• Score: 9.373581450684233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Kolmogorov-Arnold Theorem (KAT), or more generally, the Kolmogorov Superposition Theorem (KST), establishes that any non-linear multivariate function can be exactly represented as a finite superposition of non-linear univariate functions. Unlike the universal approximation theorem, which provides only an approximate representation without guaranteeing a fixed network size, KST offers a theoretically exact decomposition. The Kolmogorov-Arnold Network (KAN) was introduced as a trainable model to implement KAT, and recent advancements have adapted KAN using concepts from modern neural networks. However, KAN struggles to effectively model physical systems that require inherent equivariance or invariance to $E(3)$ transformations, a key property for many scientific and engineering applications. In this work, we propose a novel extension of KAT and KAN to incorporate equivariance and invariance over $O(n)$ group actions, enabling accurate and efficient modeling of these systems. Our approach provides a unified approach that bridges the gap between mathematical theory and practical architectures for physical systems, expanding the applicability of KAN to a broader class of problems.

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