Procedia Computer Science, volume 156, pages 367-376
Discovery of the data-driven differential equation-based models of continuous metocean process
Publication type: Journal Article
Publication date: 2019-09-26
Journal:
Procedia Computer Science
Quartile SCImago
— Quartile WOS
—
Impact factor: —
ISSN: 18770509
General Medicine
Abstract
Data-driven models are widely used in cases when the model structure is not known a priori. However, most contemporary data-driven modeling methods like neural networks and most types of simple regression cannot be interpreted. Decision trees are on contrary, interpretable, however, the model structure remains simple and thus interpretable complex models are barely obtained with decision trees. The possible tradeoff between the model complexity and interpretability is the data-driven differential equations discovery methods. At the current time, most of the methods are not able to handle the data with a significant noise level. In the paper, a new approach to the problem is proposed. The approach involves evolutionary algorithm and sparse regression and allows one to obtain various forms of equations, defined only by the number of meta-parameters instead of the pre-defined library of terms. The application of PDE discovery tool to obtain continuous metocean process equation is described.
Citations by journals
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Journal of Physics: Conference Series
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Journal of Physics: Conference Series
1 publication, 50%
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Computers and Mathematics with Applications
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Computers and Mathematics with Applications
1 publication, 50%
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Citations by publishers
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IOP Publishing
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IOP Publishing
1 publication, 50%
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Elsevier
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Elsevier
1 publication, 50%
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Maslyaev M., Hvatov A. Discovery of the data-driven differential equation-based models of continuous metocean process // Procedia Computer Science. 2019. Vol. 156. pp. 367-376.
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Maslyaev M., Hvatov A. Discovery of the data-driven differential equation-based models of continuous metocean process // Procedia Computer Science. 2019. Vol. 156. pp. 367-376.
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TY - JOUR
DO - 10.1016/j.procs.2019.08.213
UR - https://doi.org/10.1016%2Fj.procs.2019.08.213
TI - Discovery of the data-driven differential equation-based models of continuous metocean process
T2 - Procedia Computer Science
AU - Maslyaev, Mikhail
AU - Hvatov, Alexander
PY - 2019
DA - 2019/09/26 00:00:00
PB - Elsevier
SP - 367-376
VL - 156
SN - 1877-0509
ER -
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@article{2019_Maslyaev,
author = {Mikhail Maslyaev and Alexander Hvatov},
title = {Discovery of the data-driven differential equation-based models of continuous metocean process},
journal = {Procedia Computer Science},
year = {2019},
volume = {156},
publisher = {Elsevier},
month = {sep},
url = {https://doi.org/10.1016%2Fj.procs.2019.08.213},
pages = {367--376},
doi = {10.1016/j.procs.2019.08.213}
}
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