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Modeling the influence of the composition of refractory elements on the heat resistance of nickel alloys by a deep learning artificial neural network. / Tarasov, Dmitry A.; Milder, Oleg B.; Tiagunov, Andrei G.
в: Mathematical Methods in the Applied Sciences, Том 45, № 15, 10.2022, стр. 8809-8818.

Результаты исследований: Вклад в журнал › Статья › Рецензирование

Harvard

Tarasov, DA, Milder, OB & Tiagunov, AG 2022, 'Modeling the influence of the composition of refractory elements on the heat resistance of nickel alloys by a deep learning artificial neural network', Mathematical Methods in the Applied Sciences, Том. 45, № 15, стр. 8809-8818. https://doi.org/10.1002/mma.7524

APA

Tarasov, D. A., Milder, O. B., & Tiagunov, A. G. (2022). Modeling the influence of the composition of refractory elements on the heat resistance of nickel alloys by a deep learning artificial neural network. Mathematical Methods in the Applied Sciences, 45(15), 8809-8818. https://doi.org/10.1002/mma.7524

Vancouver

Tarasov DA, Milder OB, Tiagunov AG. Modeling the influence of the composition of refractory elements on the heat resistance of nickel alloys by a deep learning artificial neural network. Mathematical Methods in the Applied Sciences. 2022 окт.;45(15):8809-8818. doi: 10.1002/mma.7524

Author

Tarasov, Dmitry A. ; Milder, Oleg B. ; Tiagunov, Andrei G. / Modeling the influence of the composition of refractory elements on the heat resistance of nickel alloys by a deep learning artificial neural network. в: Mathematical Methods in the Applied Sciences. 2022 ; Том 45, № 15. стр. 8809-8818.

BibTeX

@article{682379cfd85a4901ab23b6961ac62d86,
title = "Modeling the influence of the composition of refractory elements on the heat resistance of nickel alloys by a deep learning artificial neural network",
keywords = "deep learning, heat resistance, modeling, nickel alloys, rupture strength",
author = "Tarasov, {Dmitry A.} and Milder, {Oleg B.} and Tiagunov, {Andrei G.}",
note = "Publisher Copyright: {\textcopyright} 2021 John Wiley & Sons, Ltd.",
year = "2022",
month = oct,
doi = "10.1002/mma.7524",
language = "English",
volume = "45",
pages = "8809--8818",
journal = "Mathematical Methods in the Applied Sciences",
issn = "0170-4214",
publisher = "John Wiley & Sons Inc.",
number = "15",

}

RIS

TY - JOUR

T1 - Modeling the influence of the composition of refractory elements on the heat resistance of nickel alloys by a deep learning artificial neural network

AU - Tarasov, Dmitry A.

AU - Milder, Oleg B.

AU - Tiagunov, Andrei G.

N1 - Publisher Copyright: © 2021 John Wiley & Sons, Ltd.

PY - 2022/10

Y1 - 2022/10

KW - deep learning

KW - heat resistance

KW - modeling

KW - nickel alloys

KW - rupture strength

UR - http://www.scopus.com/inward/record.url?scp=85106237156&partnerID=8YFLogxK

UR - https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=tsmetrics&SrcApp=tsm_test&DestApp=WOS_CPL&DestLinkType=FullRecord&KeyUT=000653157100001

U2 - 10.1002/mma.7524

DO - 10.1002/mma.7524

M3 - Article

AN - SCOPUS:85106237156

VL - 45

SP - 8809

EP - 8818

JO - Mathematical Methods in the Applied Sciences

JF - Mathematical Methods in the Applied Sciences

SN - 0170-4214

IS - 15

ER -

ID: 30896473