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Reconstruction of Ordinary Differential Equations from Irregularly Distributed Time-Series Data. / Golovkina, A.G. ; Kozynchenko, V.A. ; Kulabukhova, N.V. .

Proceedings of the 9th International Conference "Distributed Computing and Grid Technologies in Science and Education" (GRID'2021), Dubna, Russia, July 5-9, 2021 . ред. / Vladimir Korenkov; Andrey Nechaevskiy; Tatiana Zaikina. Том 3041 RWTH Aahen University, 2021. стр. 342-347 (CEUR Workshop Proceedings; Том 3041).

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаяРецензирование

Harvard

Golovkina, AG, Kozynchenko, VA & Kulabukhova, NV 2021, Reconstruction of Ordinary Differential Equations from Irregularly Distributed Time-Series Data. в V Korenkov, A Nechaevskiy & T Zaikina (ред.), Proceedings of the 9th International Conference "Distributed Computing and Grid Technologies in Science and Education" (GRID'2021), Dubna, Russia, July 5-9, 2021 . Том. 3041, CEUR Workshop Proceedings, Том. 3041, RWTH Aahen University, стр. 342-347, 9th International Conference "Distributed Computing and Grid-Technologies in Science and Education", GRID 2021, Dubna, Российская Федерация, 5/07/21.

APA

Golovkina, A. G., Kozynchenko, V. A., & Kulabukhova, N. V. (2021). Reconstruction of Ordinary Differential Equations from Irregularly Distributed Time-Series Data. в V. Korenkov, A. Nechaevskiy, & T. Zaikina (Ред.), Proceedings of the 9th International Conference "Distributed Computing and Grid Technologies in Science and Education" (GRID'2021), Dubna, Russia, July 5-9, 2021 (Том 3041, стр. 342-347). (CEUR Workshop Proceedings; Том 3041). RWTH Aahen University.

Vancouver

Golovkina AG, Kozynchenko VA, Kulabukhova NV. Reconstruction of Ordinary Differential Equations from Irregularly Distributed Time-Series Data. в Korenkov V, Nechaevskiy A, Zaikina T, Редакторы, Proceedings of the 9th International Conference "Distributed Computing and Grid Technologies in Science and Education" (GRID'2021), Dubna, Russia, July 5-9, 2021 . Том 3041. RWTH Aahen University. 2021. стр. 342-347. (CEUR Workshop Proceedings).

Author

Golovkina, A.G. ; Kozynchenko, V.A. ; Kulabukhova, N.V. . / Reconstruction of Ordinary Differential Equations from Irregularly Distributed Time-Series Data. Proceedings of the 9th International Conference "Distributed Computing and Grid Technologies in Science and Education" (GRID'2021), Dubna, Russia, July 5-9, 2021 . Редактор / Vladimir Korenkov ; Andrey Nechaevskiy ; Tatiana Zaikina. Том 3041 RWTH Aahen University, 2021. стр. 342-347 (CEUR Workshop Proceedings).

BibTeX

@inproceedings{c671bedde969480e80dc136d2d5dfdd8,
title = "Reconstruction of Ordinary Differential Equations from Irregularly Distributed Time-Series Data",
abstract = "The present paper aims to develop a reconstruction method for the right side of a system of ODEs in polynomial form from sparse and irregularly distributed time-series data. This method doesn{\textquoteright}t require any additional knowledge about the system and has several steps. The scarcity of the data through the trajectory length is compensated by the artificially generated points using approximating trigonometrical polynomials. Then, we get uniformly spread data points with the step conditioned by the desired accuracy of derivatives approximation in ODEs. This let to further use conventional reconstruction algorithms described in the literature. We test the proposed method on time series data generated from known ODE models in a two-dimensional system. We quantify the accuracy of the reconstruction for the system of ODEs as a function of the amount of data used by the method. Further, we solve the reconstructed system of ODEs and compare the solution to the original time series data. The method developed and validated here can now be applied to large data sets for physical and biological systems for which there is no known system of ODEs.",
author = "A.G. Golovkina and V.A. Kozynchenko and N.V. Kulabukhova",
year = "2021",
month = dec,
day = "13",
language = "English",
volume = "3041",
series = "CEUR Workshop Proceedings",
publisher = "RWTH Aahen University",
pages = "342--347",
editor = "Vladimir Korenkov and Andrey Nechaevskiy and Tatiana Zaikina",
booktitle = "Proceedings of the 9th International Conference {"}Distributed Computing and Grid Technologies in Science and Education{"} (GRID'2021), Dubna, Russia, July 5-9, 2021",
address = "Germany",
note = "9th International Conference {"}Distributed Computing and Grid Technologies in Science and Education{"}, GRID 2021 ; Conference date: 05-07-2021 Through 09-07-2021",
url = "https://indico.jinr.ru/event/1086/overview",

}

RIS

TY - GEN

T1 - Reconstruction of Ordinary Differential Equations from Irregularly Distributed Time-Series Data

AU - Golovkina, A.G.

AU - Kozynchenko, V.A.

AU - Kulabukhova, N.V.

N1 - Conference code: 9

PY - 2021/12/13

Y1 - 2021/12/13

N2 - The present paper aims to develop a reconstruction method for the right side of a system of ODEs in polynomial form from sparse and irregularly distributed time-series data. This method doesn’t require any additional knowledge about the system and has several steps. The scarcity of the data through the trajectory length is compensated by the artificially generated points using approximating trigonometrical polynomials. Then, we get uniformly spread data points with the step conditioned by the desired accuracy of derivatives approximation in ODEs. This let to further use conventional reconstruction algorithms described in the literature. We test the proposed method on time series data generated from known ODE models in a two-dimensional system. We quantify the accuracy of the reconstruction for the system of ODEs as a function of the amount of data used by the method. Further, we solve the reconstructed system of ODEs and compare the solution to the original time series data. The method developed and validated here can now be applied to large data sets for physical and biological systems for which there is no known system of ODEs.

AB - The present paper aims to develop a reconstruction method for the right side of a system of ODEs in polynomial form from sparse and irregularly distributed time-series data. This method doesn’t require any additional knowledge about the system and has several steps. The scarcity of the data through the trajectory length is compensated by the artificially generated points using approximating trigonometrical polynomials. Then, we get uniformly spread data points with the step conditioned by the desired accuracy of derivatives approximation in ODEs. This let to further use conventional reconstruction algorithms described in the literature. We test the proposed method on time series data generated from known ODE models in a two-dimensional system. We quantify the accuracy of the reconstruction for the system of ODEs as a function of the amount of data used by the method. Further, we solve the reconstructed system of ODEs and compare the solution to the original time series data. The method developed and validated here can now be applied to large data sets for physical and biological systems for which there is no known system of ODEs.

UR - http://ceur-ws.org/Vol-3041/342-347-paper-63.pdf

M3 - Conference contribution

VL - 3041

T3 - CEUR Workshop Proceedings

SP - 342

EP - 347

BT - Proceedings of the 9th International Conference "Distributed Computing and Grid Technologies in Science and Education" (GRID'2021), Dubna, Russia, July 5-9, 2021

A2 - Korenkov, Vladimir

A2 - Nechaevskiy, Andrey

A2 - Zaikina, Tatiana

PB - RWTH Aahen University

T2 - 9th International Conference "Distributed Computing and Grid Technologies in Science and Education", GRID 2021

Y2 - 5 July 2021 through 9 July 2021

ER -

ID: 87925616