Hierarchical RBF-KAN Architectures for Function Approximation
AFBytes Brief
Hierarchical RBF-KAN and RBF-SKAN networks are proposed for learning complex functions and random fields. The architectures combine radial basis functions with Kolmogorov-Arnold network principles. Evaluations cover both deterministic and stochastic approximation scenarios.
Why this matters
New neural architectures may enhance performance on scientific computing and simulation problems.
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Advances in domestic computational methods could support U.S. technological self-reliance over time.
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Improved optimization methods may contribute to more efficient modeling in defense-related computing applications.
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