@inproceedings{2a936dbb24ea40a296531307e9916e54,
title = "System Stabilization of PDEs using Physics-Informed Neural Networks (PINNs)",
abstract = "As a popular neural network model for solving forward and inverse problems in partial differential equation (PDE) control, Physics-Informed Neural Networks (PINNs) have received extensive attention in recent years and have made break-throughs in various fields. With the application of PINNs being extended to optimal control problems constrained by PDEs, where the control PDE is fully known, the problem objective is to find a control variable to minimize the desired cost objective. In this paper, with the idea of using PINNs to solve optimal control problems, we investigated effective methods to find boundary control and distributed control which can drive the PDE state towards unstable zero-point solutions. We also demonstrated the effectiveness of boundary control and distributed control through numerous numerical experiments on Reaction-Diffusion equations and Burgers' equations.",
keywords = "Deep learning, Optimal control, Physics-Informed neural network",
author = "Yuandong Cao and So, {Chi Chiu} and Junmin Wang and Yung, {Siu Pang}",
note = "Publisher Copyright: {\textcopyright} 2024 Technical Committee on Control Theory, Chinese Association of Automation.; 43rd Chinese Control Conference, CCC 2024 ; Conference date: 28-07-2024 Through 31-07-2024",
year = "2024",
doi = "10.23919/CCC63176.2024.10662626",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "8759--8764",
editor = "Jing Na and Jian Sun",
booktitle = "Proceedings of the 43rd Chinese Control Conference, CCC 2024",
address = "United States",
}