About: Learning Hidden States in a Chaotic System: A Physics-Informed Echo State Network Approach

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Learning Hidden States in a Chaotic System: A Physics-Informed Echo State Network Approach
Creator	Anh, Nguyen Magri, Luca Doan, Khoa Polifke, Wolfgang
Source	PMC
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.
has issue date	2020-05-25(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50433-5_9
has license	no-cc
sha1sum (hex)	b28f9805d004d2ba82bb1b5fd6ac8a456bd246b6
schema:url	https://doi.org/10.1007/978-3-030-50433-5_9
resource representing a document's title	Learning Hidden States in a Chaotic System: A Physics-Informed Echo State Network Approach
has PubMed Central identifier	PMC7304749
schema:publication	Computational Science - ICCS 2020
resource representing a document's body	covid:b28f9805d004d2ba82bb1b5fd6ac8a456bd246b6#body_text
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