Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Darya A. Anufrieva"'
Autor:
Darya A. Anufrieva, Charlie Oommen, VR Sanal Kumar, Victor S. Abrukov, Alexander N. Lukin, Nichith Chandrasekaran
Publikováno v:
Propellants, Explosives, Pyrotechnics. 44:579-587
Autor:
Amrith Mariappan, Thianesh U K, Darya A. Anufrieva, Alexander N. Lukin, Charlie Oommen, VR Sanal Kumar, Victor S. Abrukov, Nichith Chandrasekaran
Publikováno v:
AIAA Propulsion and Energy 2020 Forum.
Autor:
Darya A. Anufrieva, Vigneshwaran Sankar, Amrith Mariappan, Han-Lim Choi, Alexander N. Lukin, VR Sanal Kumar, Victor S. Abrukov
Publikováno v:
AIAA Propulsion and Energy 2020 Forum.
Autor:
Victor S. Abrukov, Charlie Oommen, Darya A. Anufrieva, V. R. Sanalkumar, Nichith Chandrasekaran, Alexander N. Lukin
Publikováno v:
MATEC Web of Conferences, Vol 330, p 01048 (2020)
The results of usage of data science methods, in particular artificial neural networks, for the creation of new multifactor computational models of the solid propellants (SP) combustion that solve the direct and inverse tasks are presented. The own a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::497ff1519b5345f51e68d5b9d36ce30c
Autor:
Victor S. Abrukov, Alexander N. Lukin, Nichith C, Charlie Oommen, Mikhail V. Kiselev, Darya A. Anufrieva, VR Sanal Kumar
Publikováno v:
AIAA Propulsion and Energy 2019 Forum.
Autor:
Darya A. Anufrieva, Charlie Oommen, Rajaghatta Sundararam Bharath, Victor S. Abrukov, Nichith Chandrasekaran, V. R. Sanalkumar, Alexander N. Lukin
The efforts of Russian-Indian research team for application of the data science methods, in particular, artificial neural networks for development of the multi-factor computational models for studying effects of additive’s properties on the solid r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::624aa994b68d5ed6b7feeadf01a985f9
Autor:
Charlie Oommen, A Mariappan, VR Sanalkumar, AN Lukin, Nichith Chandrasekaran, MV Kiselev, Victor S. Abrukov, Darya A. Anufrieva
Publikováno v:
53rd AIAA/SAE/ASEE Joint Propulsion Conference.
In this paper, we present the results of usage of data science methods, in particular artificial neural networks, for the creation of new multifactor computational models for prediction of burn rate of the solid propellants (SP). The analytical syste
Publikováno v:
2015 9th International Conference on Application of Information and Communication Technologies (AICT).
From the data to the Knowledge is the main topic of the paper. Knowledge-Based System is the analytical and calculation tool that: contains all relationships between all variables of the object; allows to calculate the values of one part of variables