@inproceedings{206336bb26424b8eadc931c271455593,
title = "Learning hidden states in a chaotic system: A physics-informed echo state network approach",
abstract = "We extend the Physics-Informed Echo State Network (PI-ESN) framework to reconstruct the evolution of an unmeasured state (hidden state) in a chaotic system. The PI-ESN is trained by using (i) data, which contains no information on the unmeasured state, and (ii) the physical equations of a prototypical chaotic dynamical system. Non-noisy and noisy datasets are considered. First, it is shown that the PI-ESN can accurately reconstruct the unmeasured state. Second, the reconstruction is shown to be robust with respect to noisy data, which means that the PI-ESN acts as a denoiser. This paper opens up new possibilities for leveraging the synergy between physical knowledge and machine learning to enhance the reconstruction and prediction of unmeasured states in chaotic dynamical systems.",
keywords = "Chaotic dynamical systems, Echo state networks, Physics-Informed Echo State Networks, State reconstruction",
author = "Doan, {Nguyen Anh Khoa} and Wolfgang Polifke and Luca Magri",
year = "2020",
doi = "10.1007/978-3-030-50433-5_9",
language = "English",
isbn = "9783030504328",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "117--123",
editor = "Krzhizhanovskaya, {Valeria V.} and G{\'a}bor Z{\'a}vodszky and Lees, {Michael H.} and Sloot, {Peter M.A.} and Sloot, {Peter M.A.} and Sloot, {Peter M.A.} and Dongarra, {Jack J.} and S{\'e}rgio Brissos and Jo{\~a}o Teixeira",
booktitle = "Computational Science – ICCS 2020 - 20th International Conference, Proceedings",
note = "20th International Conference on Computational Science, ICCS 2020 ; Conference date: 03-06-2020 Through 05-06-2020",
}