Integrating Kolmogorov-Arnold networks with ordinary differential equations for efficient, interpretable, and robust deep learning: Epidemiology of infectious diseases as a case study.

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 model

SUBMITTER: Ma K 

PROVIDER: S-EPMC12771098 | biostudies-literature | 2026 Jun

REPOSITORIES: biostudies-literature