Zobrazeno 1 - 10
of 26
pro vyhledávání: '"I. M. Karandashev"'
Publikováno v:
Acta Astronautica. 194:376-382
Numerical simulations of combustion processes in rocket engines requires a long run time of supercomputer systems even for a very short physical time. Therefore, creating digital twins of rocket engines needs enormous processor time, and is not very
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f6d15626b0e31184c8ef1cdd2070ef7b
https://doi.org/10.1016/j.actaastro.2021.10.034
https://doi.org/10.1016/j.actaastro.2021.10.034
Publikováno v:
Optical Memory and Neural Networks. 30:291-297
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0b93c09d6a294a1bbe11dbbd5fe919ae
https://doi.org/10.3103/s1060992x21040056
https://doi.org/10.3103/s1060992x21040056
Autor:
I. M. Karandashev, Boris Kryzhanovsky, Nickolay Smirnov, M. Yu. Malsagov, V. F. Nikitin, E.V. Mikhalchenko, V.B. Betelin
Publikováno v:
Acta Astronautica. 180:58-65
Combustion simulations are the key aspect for full-scale 3D simulations of modern and perspective engines for aerospace propulsion. The present work studies an opportunity to solve chemical kinetics problems using artificial neural networks. Using cl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2bc6cec94c76debe01f40c2b47c22c56
https://doi.org/10.1016/j.actaastro.2020.11.058
https://doi.org/10.1016/j.actaastro.2020.11.058
Autor:
I. M. Karandashev, M. M. Pushkareva
Publikováno v:
Optical Memory and Neural Networks. 29:179-186
In this article, we develop method of linear and exponential quantization of neural network weights. We improve it by means of maximizing correlations between the initial and quantized weights taking into account the weight density distribution in ea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7b59307a9ae56b01cebc2d5a3963d1fd
https://doi.org/10.3103/s1060992x20030042
https://doi.org/10.3103/s1060992x20030042
Publikováno v:
Optical Memory and Neural Networks. 28:262-270
To reduce random access memory (RAM) requirements and to increase speed of recognition algorithms we consider a weight discretization problem for trained neural networks. We show that an exponential discretization is preferable to a linear discretiza
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a9404239e6585b0dbb885650add21ef5
https://doi.org/10.3103/s1060992x19040106
https://doi.org/10.3103/s1060992x19040106
Autor:
I. M. Karandashev, Boris Kryzhanovsky
Publikováno v:
Optical Memory and Neural Networks. 28:165-174
In the present paper we analyzed a change in the density of states of a two-dimensional Ising model when a next-next-neighbor interaction is introduced. In other words, we examined two-dimensional lattices with diagonal connections. The same as in a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f653ab79ce6146fc7332bf2a5517c729
https://doi.org/10.3103/s1060992x19030032
https://doi.org/10.3103/s1060992x19030032
Autor:
Boris Kryzhanovsky, I. M. Karandashev
Publikováno v:
Optical Memory and Neural Networks. 28:89-94
A diagonally connected two-dimensional lattice is the objective of the research. The model draws interest because each of its spins has 6 connects as in a 3D lattice. On the other hand, the planarity of the model allows us to use a strict polynomial
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::877f8dfd73b07e9011a4e678461d0e44
https://doi.org/10.3103/s1060992x19020073
https://doi.org/10.3103/s1060992x19020073
For the 2D Ising model, we analyzed dependences of thermodynamic characteristics on number of spins by means of computer simulations. We compared experimental data obtained using the Fisher-Kasteleyn algorithm on a square lattice with $N=l{\times}l$
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70fe88559324baf31e8d99abf16e3566
Publikováno v:
Optical Memory and Neural Networks. 27:147-151
The paper gives the results of a numerical simulation of a two-dimensional Ising model built on finite lattices of dimension L = 50, 100, …, 500. Approximate analytical formulae for the spectral energy density are offered. Derived from Onsager’s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f301bd3a3d31036db7be0120d0b2da2e
https://doi.org/10.3103/s1060992x18030025
https://doi.org/10.3103/s1060992x18030025
Publikováno v:
Optical Memory and Neural Networks. 26:165-171
Numerical methods are used to examine the thermodynamic characteristics of the twodimensional Ising model as a function of the number of spins N. Onsager’s solution is generalized to a finite-size lattice, and experimentally validated analytical ex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3285bb7c6986044e6b5699c5b073cd7a
https://doi.org/10.3103/s1060992x17030031
https://doi.org/10.3103/s1060992x17030031