Open Access
Lecture Notes in Computer Science, volume 11540 LNCS, pages 635-641
Data-driven partial derivative equations discovery with evolutionary approach
Publication type: Book Chapter
Publication date: 2019-06-07
Journal:
Lecture Notes in Computer Science
Quartile SCImago
Q3
Quartile WOS
—
Impact factor: —
ISSN: 03029743, 16113349, 18612075, 18612083
Abstract
The data-driven models are able to study the model structure in cases when a priori information is not sufficient to build other types of models. The possible way to obtain physical interpretation is the data-driven differential equation discovery techniques. The existing methods of PDE (partial derivative equations) discovery are bound with the sparse regression. However, sparse regression is restricting the resulting model form, since the terms for PDE are defined before regression. The evolutionary approach, described in the article, has a symbolic regression as the background instead and thus has fewer restrictions on the PDE form. The evolutionary method of PDE discovery (EPDE) is tested on several canonical PDEs. The question of robustness is examined on a noised data example.
Citations by journals
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Lecture Notes in Computer Science
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Lecture Notes in Computer Science
1 publication, 14.29%
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Research
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Citations by publishers
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2
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Springer Nature
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Springer Nature
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Wiley
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1 publication, 14.29%
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Taylor & Francis
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Taylor & Francis
1 publication, 14.29%
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American Association for the Advancement of Science (AAAS)
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American Association for the Advancement of Science (AAAS)
1 publication, 14.29%
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IEEE
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IEEE
1 publication, 14.29%
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Maslyaev M., Hvatov A., Kalyuzhnaya A. Data-driven partial derivative equations discovery with evolutionary approach // Lecture Notes in Computer Science. 2019. Vol. 11540 LNCS. pp. 635-641.
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Maslyaev M., Hvatov A., Kalyuzhnaya A. Data-driven partial derivative equations discovery with evolutionary approach // Lecture Notes in Computer Science. 2019. Vol. 11540 LNCS. pp. 635-641.
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TY - GENERIC
DO - 10.1007/978-3-030-22750-0_61
UR - https://doi.org/10.1007%2F978-3-030-22750-0_61
TI - Data-driven partial derivative equations discovery with evolutionary approach
T2 - Lecture Notes in Computer Science
AU - Maslyaev, Mikhail
AU - Hvatov, Alexander
AU - Kalyuzhnaya, Anna
PY - 2019
DA - 2019/06/07 00:00:00
PB - Springer Nature
SP - 635-641
VL - 11540 LNCS
SN - 0302-9743
SN - 1611-3349
SN - 1861-2075
SN - 1861-2083
ER -
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@incollection{2019_Maslyaev,
author = {Mikhail Maslyaev and Alexander Hvatov and Anna Kalyuzhnaya},
title = {Data-driven partial derivative equations discovery with evolutionary approach},
publisher = {Springer Nature},
year = {2019},
volume = {11540 LNCS},
pages = {635--641},
month = {jun}
}