A practitioner’s guide to Kolmogorov–Arnold networks
Kolmogorov–Arnold Networks (KANs), whose design is inspired—rather than dictated— by the Kolmogorov superposition theorem, have emerged as a structured alternative to MLPs. This review provides a systematic and comprehensive overview of the rapidly expanding KAN literature. The review is organized a...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:dnet:idus________::b18162e0adb9399fc1cae3e8ea2f8506 |
| Acceso en línea: | https://hdl.handle.net/11441/187131 https://doi.org/10.1016/j.cosrev.2026.100991 |
| Access Level: | acceso abierto |
| Palabra clave: | Kolmogorov–arnold networks Kolmogorov superposition theorem Basis functions Kernel methods Neural network architectures Physics-informed learning Function approximation |
| Sumario: | Kolmogorov–Arnold Networks (KANs), whose design is inspired—rather than dictated— by the Kolmogorov superposition theorem, have emerged as a structured alternative to MLPs. This review provides a systematic and comprehensive overview of the rapidly expanding KAN literature. The review is organized around three core themes: (i) clarifying the relationships between KANs and Kolmogorov superposition theory (KST), MLPs, and classical kernel methods; (ii) analyzing basis functions as a central design axis; and (iii) summarizing recent advances in accuracy, efficiency, regularization, and convergence. Finally, we provide a practical “Choose–Your–KAN” guide and outline open research challenges and future directions. The accompanying GitHub repository (https://github.com/AmirNoori68/kan-review) serves as a structured reference for ongoing KAN research. |
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