Open Access
Lecture Notes in Computer Science, volume 12138 LNCS, pages 86-100
Data-driven partial differential equations discovery approach for the noised multi-dimensional data
Publication type: Book Chapter
Publication date: 2020-06-18
Journal:
Lecture Notes in Computer Science
Quartile SCImago
Q3
Quartile WOS
—
Impact factor: —
ISSN: 03029743, 16113349, 18612075, 18612083
Abstract
Data-driven methods provide model creation tools for systems, where the application of conventional analytical methods is restrained. The proposed method involves the data-driven derivation of a partial differential equation (PDE) for process dynamics, which can be helpful both for process simulation and studying. The paper describes the progress made within the PDE discovery framework. The framework involves a combination of evolutionary algorithms and sparse regression. Such an approach gives more versatility in comparison with other commonly used methods of data-driven partial differential derivation by making fewer restrictions on the resulting equation. This paper highlights the algorithm features which allow the processing of data with noise, which is more similar to the real-world applications of the algorithm.
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Procedia Computer Science
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Procedia Computer Science
1 publication, 50%
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GEM - International Journal on Geomathematics
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GEM - International Journal on Geomathematics
1 publication, 50%
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Citations by publishers
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Elsevier
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Elsevier
1 publication, 50%
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Springer Nature
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Springer Nature
1 publication, 50%
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Maslyaev M., Hvatov A., Kalyuzhnaya A. Data-driven partial differential equations discovery approach for the noised multi-dimensional data // Lecture Notes in Computer Science. 2020. Vol. 12138 LNCS. pp. 86-100.
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Maslyaev M., Hvatov A., Kalyuzhnaya A. Data-driven partial differential equations discovery approach for the noised multi-dimensional data // Lecture Notes in Computer Science. 2020. Vol. 12138 LNCS. pp. 86-100.
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TY - GENERIC
DO - 10.1007/978-3-030-50417-5_7
UR - https://doi.org/10.1007%2F978-3-030-50417-5_7
TI - Data-driven partial differential equations discovery approach for the noised multi-dimensional data
T2 - Lecture Notes in Computer Science
AU - Maslyaev, Mikhail
AU - Hvatov, Alexander
AU - Kalyuzhnaya, Anna
PY - 2020
DA - 2020/06/18 00:00:00
PB - Springer Nature
SP - 86-100
VL - 12138 LNCS
SN - 0302-9743
SN - 1611-3349
SN - 1861-2075
SN - 1861-2083
ER -
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@incollection{2020_Maslyaev,
author = {Mikhail Maslyaev and Alexander Hvatov and Anna Kalyuzhnaya},
title = {Data-driven partial differential equations discovery approach for the noised multi-dimensional data},
publisher = {Springer Nature},
year = {2020},
volume = {12138 LNCS},
pages = {86--100},
month = {jun}
}