Integrating Kolmogorov-Arnold Networks with Ordinary Differential Equations for Efficient, Interpretable and Robust Deep Learning: A Case Study in the Epidemiology of Infectious Diseases

Abstract excerpt

In this study, we extend the universal differential equation (UDE) framework by integrating Kolmogorov-Arnold Network (KAN) with ordinary differential equations (ODEs), herein referred to as KAN-UDE models, to achieve efficient and interpretable deep learning for complex systems. Our case study centers on the epidemiology of emerging infectious diseases. We develop an efficient algorithm to train our proposed KAN-...

Topics

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• Biomarkers
• Clostridium difficile and Clostridium perfringens research
• Comorbidity
• Gut microbiota and health
• Mathematical and Theoretical Epidemiology and Ecology Models
• Phenotype
• Practice Guidelines as Topic
• Severe Dengue

Identifiers and source

Literature Corpus work

e21f0f7b-d1dd-506e-b32d-86ad266f5323

DOI

10.1101/2024.09.23.24314194