CVKAN: Complex-Valued Kolmogorov-Arnold Networks

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.

published in proceedings of IEEE International Joint Conference on Neural Networks (IJCNN) 2025

References in corpus (7)

• Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks
• Phase Aberration Correction for in vivo Ultrasound Localization Microscopy Using a Spatiotemporal Complex-Valued Neural Network
• CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival Analysis
• A Benchmarking Study of Kolmogorov-Arnold Networks on Tabular Data
• Building Blocks for a Complex-Valued Transformer Architecture
• KAN You See It? KANs and Sentinel for Effective and Explainable Crop Field Segmentation
• Deep Complex-valued Radial Basis Function Neural Networks and Parameter Selection