论文标题
基于学习的状态重建，用于嘈杂的拉格朗日传感下的标量双曲线PDE
Learning-based State Reconstruction for a Scalar Hyperbolic PDE under noisy Lagrangian Sensing
论文作者
论文摘要
考虑了零星测量下异质动态系统的状态重建问题。该系统由对话流以及流中的多代理网络建模粒子组成。我们根据从这些药物获得的局部测量值提出了一种使用物理知识学习的部分状态重建算法。交通密度重建被用作说明结果的示例，并表明该方法提供了有效的噪声排斥。
The state reconstruction problem of a heterogeneous dynamic system under sporadic measurements is considered. This system consists of a conversation flow together with a multi-agent network modeling particles within the flow. We propose a partial-state reconstruction algorithm using physics-informed learning based on local measurements obtained from these agents. Traffic density reconstruction is used as an example to illustrate the results and it is shown that the approach provides an efficient noise rejection.
