Learning-based State Reconstruction for a Scalar Hyperbolic PDE under noisy Lagrangian Sensing

Type
Preprint

Authors
Barreau, M.
Liu, J.
Johansson, K. H.

KAUST Grant Number
OSR-2019-CRG8-4033

Date
2020-11-19

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.

Acknowledgements
The research leading to these results is partially funded by the KAUST Office of Sponsored Research under Award No. OSR-2019-CRG8-4033, the Swedish Foundation for Strategic Research, the Swedish Research Council and Knut and Alice Wallenberg Foundation.

Publisher
arXiv

arXiv
2011.09871

Additional Links
https://arxiv.org/pdf/2011.09871

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