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Updated: Jan 13, 2026

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将科尔莫戈罗夫-阿诺德网络与普通微分方程集成为高效,可解释和强大的深度学习:传染病流行病学作为一个案例研究

Kexin Ma1, Xu Lu2, Nicola Luigi Bragazzi3

  • 1School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China.

Infectious Disease Modelling
|January 8, 2026
PubMed
概括

本研究介绍了与普通微分方程 (KAN-UDE) 集成的Kolmogorov-Arnold网络 (KAN),以实现流行病学中的高效和可解释的深度学习. KAN-UDE模型显示出卓越的性能,并重建机械模型,改善疾病趋势预测.

关键词:
深度学习是一种深度学习.流行病学 流行病学这就是为什么KAN KAN KAN KAN.这就是KAN-UDEDE.机械模型是机械模型.

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