论文标题
物理信息的极端理论的功能连接应用于数据驱动参数的发现流行病学隔室模型的发现
Physics-Informed Extreme Theory of Functional Connections Applied to Data-Driven Parameters Discovery of Epidemiological Compartmental Models
论文作者
论文摘要
在这项工作中，我们应用了一种新颖，准确，快速和健壮的物理信息神经网络框架，以发现通过参数的普通微分方程（ODE）建模的问题，称为功能连接的极端理论（X-TFC）。所提出的方法合并了两个最近开发的框架，以解决涉及参数的问题，1）功能连接理论（TFC）和2）物理知识的神经网络（PINN）。特别是，这项工作的重点是X-TFC在解决反问题中估算通过确定性方法估算流行病学分区模型的参数的能力。在这项工作中处理的流行病学隔室模型是易感感染的（SIR），易感性暴露于感染的（SEIR），以及易感性暴露于感染的反射敏感性（SEIR）。结果表明，X-TFC方法在执行数据驱动的参数中发现通过参数ODE模拟的系统的高精度和有效性较低，使用不受干扰和扰动的数据发现了系统。
In this work we apply a novel, accurate, fast, and robust physics-informed neural network framework for data-driven parameters discovery of problems modeled via parametric ordinary differential equations (ODEs) called the Extreme Theory of Functional Connections (X-TFC). The proposed method merges two recently developed frameworks for solving problems involving parametric DEs, 1) the Theory of Functional Connections (TFC) and 2) the Physics-Informed Neural Networks (PINN). In particular, this work focuses on the capability of X-TFC in solving inverse problems to estimate the parameters governing the epidemiological compartmental models via a deterministic approach. The epidemiological compartmental models treated in this work are Susceptible-Infectious-Recovered (SIR), Susceptible-Exposed-Infectious-Recovered (SEIR), and Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIR). The results show the low computational times, the high accuracy and effectiveness of the X-TFC method in performing data-driven parameters discovery of systems modeled via parametric ODEs using unperturbed and perturbed data.
