论文标题
用于流行病学中可识别室模型的修改后的PINN方法，并应用于COVID-19
A Modified PINN Approach for Identifiable Compartmental Models in Epidemiology with Applications to COVID-19
论文作者
论文摘要
使用隔室模型的多种方法已被用来研究Covid-19的大流行，而使用这些模型的机器学习方法的使用取得了特别明显的成功。我们在这里提出了一种使用“物理知情的神经网络”（PINN）的变体来分析Covid-19美国开发的可访问数据的方法，该方法能够使用模型的知识来帮助学习。我们说明了使用标准PINN方法的挑战，然后在我们的信息不完整的情况下，如何对网络进行适当和新颖的修改，即使网络也可以很好地执行。还评估了模型参数的可识别性方面，以及使用小波变换来降低可用数据的方法。最后，我们讨论了神经网络方法与不同参数值模型合作的能力，以及在估计人群中如何有效测试病例的具体应用，从而通过各自的测试提供了美国州的排名。
A variety of approaches using compartmental models have been used to study the COVID-19 pandemic and the usage of machine learning methods with these models has had particularly notable success. We present here an approach toward analyzing accessible data on Covid-19's U.S. development using a variation of the "Physics Informed Neural Networks" (PINN) which is capable of using the knowledge of the model to aid learning. We illustrate the challenges of using the standard PINN approach, then how with appropriate and novel modifications to the loss function the network can perform well even in our case of incomplete information. Aspects of identifiability of the model parameters are also assessed, as well as methods of denoising available data using a wavelet transform. Finally, we discuss the capability of the neural network methodology to work with models of varying parameter values, as well as a concrete application in estimating how effectively cases are being tested for in a population, providing a ranking of U.S. states by means of their respective testing.
