ArticleInfectious Disease Modelling2026
Integrating Kolmogorov-Arnold networks with ordinary differential equations for efficient, interpretable, and robust deep learning: Epidemiology of infectious diseases as a case study.
Article in Infectious Disease Modelling, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- KKANs: Ku̇rková-Kolmogorov-Arnold networks and their learning dynamics.Neural networks : the official journal of the International Neural Network Society · 2025Article
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Authors and funding
6 authors.
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Abstract
This study extends universal differential equation (UDE) frameworks by integrating the Kolmogorov-Arnold Network (KAN) with ordinary differential equations, referred to as KAN-UDE, to achieve efficient and interpretable deep learning. Our case study centers on the epidemiology of emerging infectious diseases. Compared to UDEs based on multi-layer perceptrons, training KAN-UDE models shows significantly improved fitting performance, as evidenced by a rapid and substantial reduction in loss. KAN-UDE models demonstrate accurate reconstruction of nonlinear functions under partial time-series training data, maintaining robustness to data sparsity. This approach enables an interpretable learning process, as KAN-UDE models were reconstructed as fully mechanistic models (RMMs). While KAN-UDE models exhibit lower robustness and accuracy when real-world data randomness is considered, RMMs predict epidemic trends robustly and accurately over much longer time windows, as KAN precisely reconstructs the mechanistic functions despite data randomness.
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