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.

翻译：将物理信息神经网络（PINN）嵌入易感-感染-康复（SIR）模型，用于理解传染病的时序演化动力学。首先，通过易感-无症状-感染-康复-死亡（SAIRD）模型数值解生成的数据验证了该方法的有效性。随后将该方法应用于德国COVID-19疫情数据，结果表明其能够准确识别并预测病毒传播趋势。研究显示，不完整的物理信息模型可高效逼近更复杂的动力学过程。因此，本工作证明了利用机器学习方法（如PINN）结合房室模型研究预测疫情动态的巨大潜力。