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Doklady Rossijskoj Akademii Nauk. Mathematika, Informatika, Processy Upravlenia, 2022, Volume 508, Pages 100–101
DOI: https://doi.org/10.31857/S2686954322070128 (Mi danma343) 		 
ADVANCED STUDIES IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Theoretical prerequisites for physically justified machine learning and its applications to fluid dynamics

A. V. Kornaeva, E. P. Kornaevab, I. N. Stebakovc

a Research Center of Artificial Intelligence, Innopolis University, Innopolis, Russia
b Department of Intelligence Systems and Digital Technologies, Orel State University, Orel, Russia
c Department of Mechatronics, Mechanics, and Robotics, Orel State University, Orel, Russia
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DOI: https://doi.org/10.31857/S2686954322070128
Abstract: Some laws of physics postulate that a quantity in the physical process under study has to take its extremal value. In this work, a generalization of a law of this kind is proposed and an approach is described in which artificial neural networks are used to minimize the power of internal forces and to simulate hydrodynamic processes for various applications.
Keywords: physically justified machine learning, deep learning, image segmentation, variational problem, objective functional.
Presented: A. L. Semenov
Received: 28.10.2022
Revised: 28.10.2022
Accepted: 01.11.2022
English version:
Doklady Mathematics, 2022, Volume 106, Issue suppl. 1, Pages S91–S92
DOI: https://doi.org/10.1134/S1064562422060126
Bibliographic databases:
Document Type: Article
UDC: 004.8
Language: Russian
Citation: A. V. Kornaev, E. P. Kornaeva, I. N. Stebakov, “Theoretical prerequisites for physically justified machine learning and its applications to fluid dynamics”, Dokl. RAN. Math. Inf. Proc. Upr., 508 (2022), 100–101; Dokl. Math., 106:suppl. 1 (2022), S91–S92
Citation in format AMSBIB
\Bibitem{KorKorSte22}
\by A.~V.~Kornaev, E.~P.~Kornaeva, I.~N.~Stebakov
\paper Theoretical prerequisites for physically justified machine learning and its applications to fluid dynamics
\jour Dokl. RAN. Math. Inf. Proc. Upr.
\yr 2022
\vol 508
\pages 100--101
\mathnet{http://mi.mathnet.ru/danma343}
\crossref{https://doi.org/10.31857/S2686954322070128}
\elib{https://elibrary.ru/item.asp?id=49991316}
\transl
\jour Dokl. Math.
\yr 2022
\vol 106
\issue suppl. 1
\pages S91--S92
\crossref{https://doi.org/10.1134/S1064562422060126}
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