4 papers
A Physics-Informed Neural Networks-Based Model Predictive Control Framework for Epidemics
Aiping Zhong, Baike She, Philip E. Paré
This work introduces a physics-informed neural networks (PINNs)-based model predictive control (MPC) framework for susceptible-infected-recovered () spreading models. Existing…
Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data
Lei Xin, Baike She, Qi Dou +2
The identification of a linear system model from data has wide applications in control theory. The existing work that provides finite sample guarantees for linear system identifica…
Scalable Distributed Reproduction Numbers of Network Epidemics with Differential Privacy
Bo Chen, Baike She, Calvin Hawkins +2
Reproduction numbers are widely used for the estimation and prediction of epidemic spreading processes over networks. However, conventional reproduction numbers of an overall netwo…
A Dissipativity Approach to Analyzing Composite Spreading Networks
Baike She, Matthew Hale
The study of spreading processes often analyzes networks at different resolutions, e.g., at the level of individuals or countries, but it is not always clear how properties at one…