Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Dmitriy Tarkhov"'
Autor:
Natalia Anosova, Aleksandra Dashkina, Aleksandra Kobicheva, Ekaterina Shostak, Dmitriy Tarkhov
Publikováno v:
European Journal of Investigation in Health, Psychology and Education, Vol 13, Iss 11, Pp 2681-2696 (2023)
In the current study, we evaluated the students’ foreign language lexical and grammatical skills in the course based on the peer teaching methodology and analyzed the effect of their altruistic and egoistic behaviors on learning results. This exper
Externí odkaz:
https://doaj.org/article/90fefff543ca452bac135c4c0605d2c5
Publikováno v:
Computation, Vol 11, Iss 9, p 168 (2023)
This article examines the possibilities of adapting approximate solutions of boundary value problems for differential equations using physics-informed neural networks (PINNs) to changes in data about the physical entity being modelled. Two types of m
Externí odkaz:
https://doaj.org/article/ab982e0b1d5043f79df20e88631e57bd
Autor:
Tatiana Lazovskaya, Dmitriy Tarkhov, Maria Chistyakova, Egor Razumov, Anna Sergeeva, Tatiana Shemyakina
Publikováno v:
Computation, Vol 11, Iss 8, p 166 (2023)
The article presents the development of new physics-informed evolutionary neural network learning algorithms. These algorithms aim to address the challenges of ill-posed problems by constructing a population close to the Pareto front. The study focus
Externí odkaz:
https://doaj.org/article/2bb6934726ed418285ceb81991938279
Publikováno v:
Современные информационные технологии и IT-образование, Vol 16, Iss 2, Pp 273-284 (2020)
The construction of multilayer approximate solutions of differential equations based on classical numerical methods is used to approximate special functions as solutions of the corresponding differential equations. In this paper, we investigate the B
Externí odkaz:
https://doaj.org/article/b5875fe774bc4f2091bc3660acfcc6ac
Publikováno v:
Sensors, Vol 23, Iss 2, p 663 (2023)
A novel type of neural network with an architecture based on physics is proposed. The network structure builds on a body of analytical modifications of classical numerical methods. A feature of the constructed neural networks is defining parameters o
Externí odkaz:
https://doaj.org/article/e7a582b96f8f43559aa121e6943da75c
Autor:
Tatiana Lazovskaya, Dmitriy Tarkhov, Alina Dudnik, Elena Koksharova, Olga Mochalova, Danil Muranov, Ksenia Pozhvanyuk, Anastasia Sysoeva
Publikováno v:
Studies in Computational Intelligence ISBN: 9783031190315
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1a9e582588c0ccd694556002781a0728
https://doi.org/10.1007/978-3-031-19032-2_42
https://doi.org/10.1007/978-3-031-19032-2_42
Publikováno v:
Studies in Computational Intelligence ISBN: 9783031190315
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5146d9eeb3a4bb810c485a407179a49c
https://doi.org/10.1007/978-3-031-19032-2_54
https://doi.org/10.1007/978-3-031-19032-2_54
Autor:
Alexandra Dashkina, Aleksandra Kobicheva, Tatiana Lazovskaya, Elena Tokareva, Dmitriy Tarkhov, Irina Guselnikova
Publikováno v:
Sustainability; Volume 14; Issue 10; Pages: 5908
(1) The main goal of this research was to assess the effectiveness of the computer-supported collaborative learning for language learning purposes using the indicators of students’ learning outcomes and the level of their engagement, as well as to
Autor:
Natalya Koreykina, Mariya R. Bortkovskaya, Alyona Bondarchuck, Kozhanova Polina, Chernaya Ekaterina, Dmitriy Tarkhov, Tatyana T. Kaverzneva
Publikováno v:
Advances in Neural Computation, Machine Learning, and Cognitive Research V ISBN: 9783030915803
Advances in Neural Computation, Machine Learning, and Cognitive Research V
Advances in Neural Computation, Machine Learning, and Cognitive Research V
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e401098295e196a92bc71544eb3d4ee7
https://doi.org/10.1007/978-3-030-91581-0_36
https://doi.org/10.1007/978-3-030-91581-0_36
Publikováno v:
Thermal Science, Vol 23, Iss Suppl. 2, Pp 583-589 (2019)
In this paper, we conduct the comparative analysis of two neural network approaches to the problem of constructing approximate neural network solutions of non-linear differential equations. The first approach is connected with building a neural netwo