Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Valentin Volokitin"'
Autor:
Yury Rodimkov, Shikha Bhadoria, Valentin Volokitin, Evgeny Efimenko, Alexey Polovinkin, Thomas Blackburn, Mattias Marklund, Arkady Gonoskov, Iosif Meyerov
Publikováno v:
Sensors, Vol 21, Iss 21, p 6982 (2021)
The power of machine learning (ML) in feature identification can be harnessed for determining quantities in experiments that are difficult to measure directly. However, if an ML model is trained on simulated data, rather than experimental results, th
Externí odkaz:
https://doaj.org/article/3c3b37f45d0047e7bd7ebe672cf0bdbb
Autor:
Elena Panova, Valentin Volokitin, Evgeny Efimenko, Julien Ferri, Thomas Blackburn, Mattias Marklund, Alexander Muschet, Aitor De Andres Gonzalez, Peter Fischer, Laszlo Veisz, Iosif Meyerov, Arkady Gonoskov
Publikováno v:
Applied Sciences, Vol 11, Iss 3, p 956 (2021)
When a pulsed, few-cycle electromagnetic wave is focused by optics with f-number smaller than two, the frequency components it contains are focused to different regions of space, building up a complex electromagnetic field structure. Accurate numeric
Externí odkaz:
https://doaj.org/article/29debc690ff04a54aec938083841dbb9
Autor:
Yury Rodimkov, Evgeny Efimenko, Valentin Volokitin, Elena Panova, Alexey Polovinkin, Iosif Meyerov, Arkady Gonoskov
Publikováno v:
Entropy, Vol 23, Iss 1, p 21 (2020)
When entering the phase of big data processing and statistical inferences in experimental physics, the efficient use of machine learning methods may require optimal data preprocessing methods and, in particular, optimal balance between details and no
Externí odkaz:
https://doaj.org/article/cb881d2f44504ad6bb73907b08ddf97b
Autor:
Iosif Meyerov, Evgeny Kozinov, Alexey Liniov, Valentin Volokitin, Igor Yusipov, Mikhail Ivanchenko, Sergey Denisov
Publikováno v:
Entropy, Vol 22, Iss 10, p 1133 (2020)
With their constantly increasing peak performance and memory capacity, modern supercomputers offer new perspectives on numerical studies of open many-body quantum systems. These systems are often modeled by using Markovian quantum master equations de
Externí odkaz:
https://doaj.org/article/6bde6c7aea324eeeac1eb26c0a8ee34d
Autor:
Alexey Liniov, M. V. Ivanchenko, I. I. Yusipov, Iosif B. Meyerov, Valentin Volokitin, Sergey Denisov
Publikováno v:
Lobachevskii Journal of Mathematics. 42:1622-1629
Most of the problems of theoretical quantum physics are characterized by a high complexity. Practically, this means that solution of such a problem demands computational effort and resources that often scale exponentially with the size of the quantum
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031229404
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::31519630636a12732cfe6092189e1107
https://doi.org/10.1007/978-3-031-22941-1_4
https://doi.org/10.1007/978-3-031-22941-1_4
Publikováno v:
Communications in Computer and Information Science ISBN: 9783031241444
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9a5699c50eaf37736071b0dee29f45c4
https://doi.org/10.1007/978-3-031-24145-1_21
https://doi.org/10.1007/978-3-031-24145-1_21
Autor:
Mattias Marklund, Iosif B. Meyerov, Alexey Polovinkin, Shikha Bhadoria, Evgeny Efimenko, Tom Blackburn, Valentin Volokitin, Arkady Gonoskov, Yury Rodimkov
Publikováno v:
Sensors
Volume 21
Issue 21
Sensors (Basel, Switzerland)
Sensors, Vol 21, Iss 6982, p 6982 (2021)
Volume 21
Issue 21
Sensors (Basel, Switzerland)
Sensors, Vol 21, Iss 6982, p 6982 (2021)
The power of machine learning (ML) in feature identification can be harnessed for determining quantities in experiments that are difficult to measure directly. However, if an ML model is trained on simulated data, rather than experimental results, th
Autor:
Evgeny Kozinov, Iosif B. Meyerov, Evgenii P. Vasiliev, Valentina Kustikova, Valentin Volokitin
Publikováno v:
Communications in Computer and Information Science ISBN: 9783030787585
The paper aims to compare the performance of deep convolutional network inference. Experiments are carried out on a high-end server with two Intel Xeon Platinum 8260L 2.4 GHz CPUs (48 cores in total). Performance analysis is done using the ResNet-50
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f2580fedbf882608058ba608a2350bfd
https://doi.org/10.1007/978-3-030-78759-2_29
https://doi.org/10.1007/978-3-030-78759-2_29
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030863586
PaCT
PaCT
New hardware architectures open up immense opportunities for supercomputer simulations. However, programming techniques for different architectures vary significantly, which leads to the necessity of developing and supporting multiple code versions,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::46e31c3b9ee8b6b1fd48f74942aed166
https://doi.org/10.1007/978-3-030-86359-3_22
https://doi.org/10.1007/978-3-030-86359-3_22