Neural network classification of eigenmodes in the magnetohydrodynamic spectroscopy code Legolas

J. De Jonghe; M. D. Kuczyński

doi:10.1007/s00521-023-09403-1

Neural network classification of eigenmodes in the magnetohydrodynamic spectroscopy code Legolas

J. De Jonghe, M. D. Kuczyński^*

^*Corresponding author for this work

Applied Mathematics

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Abstract

A neural network is employed to address a non-binary classification problem of plasma instabilities in astrophysical jets, calculated with the Legolas code. The trained models exhibit reliable performance in the identification of the two instability types supported by these jets. We also discuss the generation of artificial data and refinement of predictions in general eigenfunction classification problems.

Original language	English
Pages (from-to)	5955–5964
Number of pages	10
Journal	Neural Computing and Applications
Volume	36
Early online date	16 Jan 2024
DOIs	https://doi.org/10.1007/s00521-023-09403-1
Publication status	Published - Apr 2024

Keywords

Magnetohydrodynamics
Eigenproblem
Neural network
Supervised learning
Classification

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10.1007/s00521-023-09403-1Licence: CC BY

De-Jonghe_2024_NCA_Neural-network_CC
Copyright The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Final published version, 1.03 MBLicence: CC BY

Neural network classification of eigenmodes in the magnetohydrodynamic spectroscopy code Legolas (dataset)
De Jonghe, J. (Creator), Kaggle, 2024
https://kaggle.com/datasets/2ad64b791af42f7043d5fe778c97984ada98f30859d56269b03766ef46d7952f
Dataset

Cite this

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title = "Neural network classification of eigenmodes in the magnetohydrodynamic spectroscopy code Legolas",

abstract = "A neural network is employed to address a non-binary classification problem of plasma instabilities in astrophysical jets, calculated with the Legolas code. The trained models exhibit reliable performance in the identification of the two instability types supported by these jets. We also discuss the generation of artificial data and refinement of predictions in general eigenfunction classification problems.",

keywords = "Magnetohydrodynamics, Eigenproblem, Neural network, Supervised learning, Classification",

author = "{De Jonghe}, J. and Kuczy{\'n}ski, {M. D.}",

note = "JDJ was supported by funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme, Grant agreement No. 833251 PROMINENT ERC-ADG 2018.",

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AU - De Jonghe, J.

AU - Kuczyński, M. D.

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N2 - A neural network is employed to address a non-binary classification problem of plasma instabilities in astrophysical jets, calculated with the Legolas code. The trained models exhibit reliable performance in the identification of the two instability types supported by these jets. We also discuss the generation of artificial data and refinement of predictions in general eigenfunction classification problems.

AB - A neural network is employed to address a non-binary classification problem of plasma instabilities in astrophysical jets, calculated with the Legolas code. The trained models exhibit reliable performance in the identification of the two instability types supported by these jets. We also discuss the generation of artificial data and refinement of predictions in general eigenfunction classification problems.

KW - Magnetohydrodynamics

KW - Eigenproblem

KW - Neural network

KW - Supervised learning

KW - Classification

U2 - 10.1007/s00521-023-09403-1

DO - 10.1007/s00521-023-09403-1

M3 - Article

SN - 1433-3058

VL - 36

SP - 5955

EP - 5964

JO - Neural Computing and Applications

JF - Neural Computing and Applications

ER -

Neural network classification of eigenmodes in the magnetohydrodynamic spectroscopy code Legolas

Abstract

Keywords

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Neural network classification of eigenmodes in the magnetohydrodynamic spectroscopy code Legolas (dataset)

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