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基于物理学的神经网络集成分区模型,用于分析COVID-19传播动态

Xiao Ning1, Jinxing Guan2, Xi-An Li3

  • 1State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, 2 Sipailou, Nanjing 210096, China.

Viruses
|August 26, 2023
PubMed
概括

本研究引入了一种使用物理信息神经网络 (PINNs) 的新型人工智能方法,以准确估计传染病模型中的时间变化的参数. 该方法有效地捕捉了复杂的疾病动态,如COVID-19,以改善预测和干预策略.

关键词:
在COVID-19的传播过程中,COVID-19的传播是分隔式模型的模型.前进-反向问题基于物理学的神经网络.

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