Learning-Based State Reconstruction for a Scalar Hyperbolic PDE Under Noisy Lagrangian Sensing
Abstract
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.
Cite
Text
Barreau et al. "Learning-Based State Reconstruction for a Scalar Hyperbolic PDE Under Noisy Lagrangian Sensing." Proceedings of the 3rd Conference on Learning for Dynamics and Control, 2021.Markdown
[Barreau et al. "Learning-Based State Reconstruction for a Scalar Hyperbolic PDE Under Noisy Lagrangian Sensing." Proceedings of the 3rd Conference on Learning for Dynamics and Control, 2021.](https://mlanthology.org/l4dc/2021/barreau2021l4dc-learningbased/)BibTeX
@inproceedings{barreau2021l4dc-learningbased,
title = {{Learning-Based State Reconstruction for a Scalar Hyperbolic PDE Under Noisy Lagrangian Sensing}},
author = {Barreau, Matthieu and Liu, John and Johansson, Karl Henrik},
booktitle = {Proceedings of the 3rd Conference on Learning for Dynamics and Control},
year = {2021},
pages = {34-46},
volume = {144},
url = {https://mlanthology.org/l4dc/2021/barreau2021l4dc-learningbased/}
}