arXiv:2602.05977v2 Announce Type: replace 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.
The continuous push for more efficient and generalizable AI architectures, coupled with advances in computational algebra, drives the development of novel function approximation methods.
This research could lead to more robust and versatile AI models capable of handling complex scientific and engineering problems with greater accuracy and efficiency.
The introduction of Clifford Algebra spaces and new batch normalization strategies could significantly enhance the capabilities of neural networks in handling high-dimensional and variable-domain data.
- · AI researchers
- · Scientific computing sector
- · Engineering industries
- · Hardware manufacturers for specialized AI compute
- · Traditional neural network architectures in specific niches
- · Sectors relying on less efficient approximation methods
Improved performance and efficiency in AI models for complex tasks across various domains.
Acceleration of discovery in fields like material science, drug design, and climate modeling due to enhanced AI capabilities.
Potential for new AI-driven product categories that leverage the unique approximation strengths of such networks.
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Read at arXiv cs.LG