- author = "Mikhail Maslyaev and Alexander Hvatov and Anna V. Kalyuzhnaya",
- title = "Partial differential equations discovery with {EPDE} framework: application for real and synthetic data",
- journal = "Journal of Computational Science",
- year = "2021",
- volume = "53",
- pages = "101345",
- month = jul,
- keywords = "genetic algorithms, genetic programming, Data-driven modeling, PDE discovery, Evolutionary algorithms, sparse regression, spatial fields, physical measurement data",
- ISSN = "1877-7503",
- timestamp = "Thu, 14 Oct 2021 09:07:15 +0200",
-
biburl = "
https://dblp.org/rec/journals/jocs/MaslyaevHK21.bib",
- bibsource = "dblp computer science bibliography, https://dblp.org",
-
URL = "http://www.human-competitive.org/sites/default/files/entry_0.txt",
-
URL = "
http://www.human-competitive.org/sites/default/files/jocs_2020_0.pdf",
-
URL = "https://www.human-competitive.org/sites/default/files/entry_1.txt",
-
URL = "https://www.human-competitive.org/sites/default/files/partial_differential_equations_discovery_with_epde_framework_application_for_real_and_synthetic_data.pdf",
-
URL = "https://www.sciencedirect.com/science/article/pii/S1877750321000429",
-
DOI = "
doi:10.1016/j.jocs.2021.101345",
- size = "11 pages",
- 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, helping process simulation and study. The paper describes the methods that are used within the EPDE (Evolutionary Partial Differential Equations) partial differential equation discovery framework [1]. The framework involves a combination of evolutionary algorithms and sparse regression. Such an approach is versatile compared to other commonly used data-driven partial differential derivation methods by making fewer assumptions about the resulting equation. This paper highlights the algorithm features that allow data processing with noise, which is similar to the algorithm real-world applications. This paper is an extended version of the ICCS-2020 conference paper [2]",
-
notes = "Entered 2021 HUMIES Entered 2022 HUMIES Also known as
\cite{DBLP:journals/jocs/MaslyaevHK21}
See also [2] M. Maslyaev, A. Hvatov, A. Kalyuzhnaya, Data-driven partial differential equations discovery approach for the noised multi-dimensional data, in: International Conference on Computational Science, Springer, 2020, pp. 86-100.
ITMO University, 49 Kronverksky Pr. St. Petersburg, 197101, Russian Federation",
