Open Access
Procedia Computer Science, volume 178, pages 18-26
Discovery of the data-driven models of continuous metocean process in form of nonlinear ordinary differential equations
Publication type: Journal Article
Publication date: 2020-12-07
Journal:
Procedia Computer Science
Quartile SCImago
— Quartile WOS
—
Impact factor: —
ISSN: 18770509
General Medicine
Abstract
Data-driven surrogate models are widely used when the system dynamics equations and governing models are not known a priori. The form of the differential equation with the constant coefficients is widely used to obtain a data-driven physical process model. However, the physical models’ form is more complex, and the constant coefficients are not the case for most non-linear problems. In the article, the algorithm for the discovery of the ordinary differential equations with variable coefficients is proposed. The algorithm is based on discovering the governing equation’s structure, using a combination of sparse regression and an evolutionary algorithm. The proposed method is tested on the synthetic data, obtained from the solution of known ordinary differential equations, and on metocean data fields.
Citations by journals
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1
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Applied Sciences (Switzerland)
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Applied Sciences (Switzerland)
1 publication, 33.33%
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Processes
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Processes
1 publication, 33.33%
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Moscow University Physics Bulletin (English Translation of Vestnik Moskovskogo Universiteta, Fizika)
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Moscow University Physics Bulletin (English Translation of Vestnik Moskovskogo Universiteta, Fizika)
1 publication, 33.33%
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1
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Citations by publishers
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1
2
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Multidisciplinary Digital Publishing Institute (MDPI)
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Multidisciplinary Digital Publishing Institute (MDPI)
2 publications, 66.67%
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Pleiades Publishing
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Pleiades Publishing
1 publication, 33.33%
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1
2
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- We do not take into account publications that without a DOI.
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- Statistics recalculated weekly.
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Maslyaev M. et al. Discovery of the data-driven models of continuous metocean process in form of nonlinear ordinary differential equations // Procedia Computer Science. 2020. Vol. 178. pp. 18-26.
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Maslyaev M., Hvatov A., Kalyuzhnaya A., Kalyuzhnaya A. Discovery of the data-driven models of continuous metocean process in form of nonlinear ordinary differential equations // Procedia Computer Science. 2020. Vol. 178. pp. 18-26.
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TY - JOUR
DO - 10.1016/j.procs.2020.11.003
UR - https://doi.org/10.1016%2Fj.procs.2020.11.003
TI - Discovery of the data-driven models of continuous metocean process in form of nonlinear ordinary differential equations
T2 - Procedia Computer Science
AU - Maslyaev, Mikhail
AU - Hvatov, Alexander
AU - Kalyuzhnaya, Anna
AU - Kalyuzhnaya, Anna
PY - 2020
DA - 2020/12/07 00:00:00
PB - Elsevier
SP - 18-26
VL - 178
SN - 1877-0509
ER -
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@article{2020_Maslyaev,
author = {Mikhail Maslyaev and Alexander Hvatov and Anna Kalyuzhnaya and Anna Kalyuzhnaya},
title = {Discovery of the data-driven models of continuous metocean process in form of nonlinear ordinary differential equations},
journal = {Procedia Computer Science},
year = {2020},
volume = {178},
publisher = {Elsevier},
month = {dec},
url = {https://doi.org/10.1016%2Fj.procs.2020.11.003},
pages = {18--26},
doi = {10.1016/j.procs.2020.11.003}
}