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：首先,这一方法的实效表现在综合数据上,这些数据来自易感染-已恢复感染-已死亡(SAIRD)模型的数字解决方案;然后,该方法应用于德国报告的COVID-19数据,表明它能够准确地识别和预测病毒传播趋势;结果显示,不完全的物理知情模型可以有效地处理更复杂的动态;因此,目前的工作表明,使用机器学习方法(例如PINNs)与分机模型相结合研究和预测流行病动态的高度潜力。