用物理信息神经网络逼近 COVID-19 的流行病学动力学
Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks
AI总结：
本文提出将嵌入 SIR 模型的物理信息神经网络（PINN）用于传染病动力学建模，通过在合成数据和德国 COVID-19 数据上的实验证明其能准确识别和预测疫情传播趋势，展示了机器学习与仓室模型结合研究流行病的高潜力。
摘要：
设计了一种嵌入易感-感染-移除（SIR）模型的物理信息神经网络（PINN），用于理解传染病的时间演化动力学。首先，在由易感-无症状-感染-康复-死亡（SAIRD）模型数值解生成的合成数据上证明了该方法的有效性。然后，将该方法应用于德国报告的 COVID-19 数据，并表明它能够准确识别和预测病毒传播趋势。结果表明，不完整的物理信息模型可以有效地逼近更复杂的动力学。因此，本工作展示了使用机器学习方法（例如 PINN）结合仓室模型来研究和预测流行病动力学的高潜力。
英文摘要：
A physics-informed neural network (PINN) embedded with the susceptible-infected-removed (SIR) model is devised to understand the temporal evolution dynamics of infectious diseases. Firstly, the effectiveness of this approach is demonstrated on synthetic data as generated from the numerical solution of the susceptible-asymptomatic-infected-recovered-dead (SAIRD) model. Then, the method is applied to COVID-19 data reported for Germany and shows that it can accurately identify and predict virus spread trends. The results indicate that an incomplete physics-informed model can approach more complicated dynamics efficiently. Thus, the present work demonstrates the high potential of using machine learning methods, e.g., PINNs, to study and predict epidemic dynamics in combination with compartmental models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
