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Suitability of different machine learning methods for high-speed flow modeling issues. / Истомин, Владимир Андреевич; Павлов, Семён Анатольевич.

In: Cybernetics and Physics, Vol. 12, No. 4, 31.12.2023, p. 264-274.

Research output: Contribution to journal › Article › peer-review

Harvard

Истомин, ВА & Павлов, СА 2023, 'Suitability of different machine learning methods for high-speed flow modeling issues', Cybernetics and Physics, vol. 12, no. 4, pp. 264-274. https://doi.org/10.35470/2226-4116-2023-12-4-264-274

APA

Истомин, В. А., & Павлов, С. А. (2023). Suitability of different machine learning methods for high-speed flow modeling issues. Cybernetics and Physics, 12(4), 264-274. https://doi.org/10.35470/2226-4116-2023-12-4-264-274

Vancouver

Истомин ВА, Павлов СА. Suitability of different machine learning methods for high-speed flow modeling issues. Cybernetics and Physics. 2023 Dec 31;12(4):264-274. https://doi.org/10.35470/2226-4116-2023-12-4-264-274

Author

Истомин, Владимир Андреевич ; Павлов, Семён Анатольевич. / Suitability of different machine learning methods for high-speed flow modeling issues. In: Cybernetics and Physics. 2023 ; Vol. 12, No. 4. pp. 264-274.

BibTeX

@article{bf991fac852940439b2c384c1b0122e3,
title = "Suitability of different machine learning methods for high-speed flow modeling issues",
author = "Истомин, {Владимир Андреевич} and Павлов, {Семён Анатольевич}",
year = "2023",
month = dec,
day = "31",
doi = "10.35470/2226-4116-2023-12-4-264-274",
language = "English",
volume = "12",
pages = "264--274",
journal = "Cybernetics and Physics",
issn = "2223-7038",
publisher = "IPACS",
number = "4",

}

RIS

TY - JOUR

T1 - Suitability of different machine learning methods for high-speed flow modeling issues

AU - Истомин, Владимир Андреевич

AU - Павлов, Семён Анатольевич

PY - 2023/12/31

Y1 - 2023/12/31

U2 - 10.35470/2226-4116-2023-12-4-264-274

DO - 10.35470/2226-4116-2023-12-4-264-274

M3 - Article

VL - 12

SP - 264

EP - 274

JO - Cybernetics and Physics

JF - Cybernetics and Physics

SN - 2223-7038

IS - 4

ER -

ID: 115397537