arXiv cs.AI· Matthias Wolff, Francesco Alesiani, Christof Duhme, Xiaoyi Jiang·· 12 小时前AI 评分20
Clifford Kolmogorov-Arnold 网络（ClKAN）：任意 Clifford 代数空间中的高效函数逼近架构
Clifford Kolmogorov-Arnold Networks
AI 导读
研究者提出 Clifford Kolmogorov-Arnold Network（ClKAN），一种可在任意 Clifford 代数空间进行函数逼近的灵活高效架构。该工作采用 Randomized Quasi-Monte Carlo 网格生成来应对高维代数带来的指数级扩展问题，并引入新的批归一化策略处理可变域输入。ClKAN 在合成任务与物理启发任务上完成验证，论文已发表于 IJCNN 2026。
正文 · 原文
Abstract:We introduce Clifford Kolmogorov-Arnold Network (ClKAN), a flexible and efficient architecture for function approximation in arbitrary Clifford Algebra spaces. We propose the use of Randomized Quasi-Monte Carlo grid generation as a solution to the exponential scaling associated with higher-dimensional algebras. Our ClKAN also introduces new batch normalization strategies to deal with variable domain input. ClKAN finds application in scientific discovery and engineering, and is validated in synthetic and physics-inspired tasks.
| Comments: | published in proceedings of IEEE International Joint Conference on Neural Networks (IJCNN) 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2602.05977 [cs.LG] |
| (or arXiv:2602.05977v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2602.05977 arXiv-issued DOI via DataCite |
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| Related DOI: | https://doi.org/10.1109/IJCNN69854.2026.11728617
DOI(s) linking to related resources |
Submission history
From: Matthias Wolff [view email]
[v1]
Thu, 5 Feb 2026 18:25:40 UTC (315 KB)
[v2]
Tue, 23 Jun 2026 22:02:43 UTC (321 KB)
[v3]
Fri, 23 Oct 2026 14:03:55 UTC (323 KB)
来源：arXiv cs.AI · arxiv.org