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arXiv · 2502.02417

CVKAN: Complex-Valued Kolmogorov-Arnold Networks

Matthias Wolff·Florian Eilers·Xiaoyi Jiang
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Abstract

In this work we propose CVKAN, a complex-valued Kolmogorov-Arnold Network (KAN), to join the intrinsic interpretability of KANs and the advantages of Complex-Valued Neural Networks (CVNNs). We show how to transfer a KAN and the necessary associated mechanisms into the complex domain. To confirm that CVKAN meets expectations we conduct experiments on symbolic complex-valued function fitting and physically meaningful formulae as well as on a more realistic dataset from knot theory. Our proposed CVKAN is more stable and performs on par or better than real-valued KANs while requiring less parameters and a shallower network architecture, making it more explainable.

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Matthias Wolff, Florian Eilers, Xiaoyi Jiang. 2025-02-04. CVKAN: Complex-Valued Kolmogorov-Arnold Networks. https://doi.org/10.1109/ijcnn64981.2025.11227425

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