Zobrazeno 1 - 10
of 216
pro vyhledávání: '"Litvinov Sergey"'
Autor:
Alexeev, Dmitry, Litvinov, Sergey, Economides, Athena, Amoudruz, Lucas, Toner, Mehmet, Koumoutsakos, Petros
The identification of cells and particles based on their transport properties in microfluidic devices is crucial for numerous applications in biology and medicine. Neutrally buoyant particles transported in microfluidic channels, migrate laterally to
Externí odkaz:
http://arxiv.org/abs/2408.09552
Biomedical applications such as targeted drug delivery, microsurgery or sensing rely on reaching precise areas within the body in a minimally invasive way. Artificial bacterial flagella (ABFs) have emerged as potential tools for this task by navigati
Externí odkaz:
http://arxiv.org/abs/2404.02171
Autor:
Weidner, Jonas, Ezhov, Ivan, Balcerak, Michal, Metz, Marie-Christin, Litvinov, Sergey, Kaltenbach, Sebastian, Feiner, Leonhard, Lux, Laurin, Kofler, Florian, Lipkova, Jana, Latz, Jonas, Rueckert, Daniel, Menze, Bjoern, Wiestler, Benedikt
Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a
Externí odkaz:
http://arxiv.org/abs/2403.04500
Autor:
Balcerak, Michal, Weidner, Jonas, Karnakov, Petr, Ezhov, Ivan, Litvinov, Sergey, Koumoutsakos, Petros, Zhang, Ray Zirui, Lowengrub, John S., Wiestler, Bene, Menze, Bjoern
Brain tumor growth is unique to each glioma patient and extends beyond what is visible in imaging scans, infiltrating surrounding brain tissue. Understanding these hidden patient-specific progressions is essential for effective therapies. Current tre
Externí odkaz:
http://arxiv.org/abs/2312.05063
Publikováno v:
Eur. Phys. J. E 46, 59 (2023)
We present a potent computational method for the solution of inverse problems in fluid mechanics. We consider inverse problems formulated in terms of a deterministic loss function that can accommodate data and regularization terms. We introduce a mul
Externí odkaz:
http://arxiv.org/abs/2303.04679
Publikováno v:
Nuclear Technology and Radiation Protection, Vol 27, Iss 2, Pp 107-112 (2012)
Short-lived exotic nuclei can be produced and separated with the high-energy nuclear beam facility called fragment separator at the Centre for Heavy Ion Research. These nuclides can be injected and stored in the storage ring called experimental stora
Externí odkaz:
https://doaj.org/article/c0eea176a097433b907827efd68fcb55
Autor:
Litvinov Sergey N., Lebedev Vladimir D., Smirnov Nikolay N., Tyutikov Vladimir V., Makhsumov Ilkhom B.
Publikováno v:
MATEC Web of Conferences, Vol 194, p 01035 (2018)
This study examines the results of thermal tests on a 6(10) kV digital combined current and voltage transformer conducted in an environmental chamber. This measuring instrument consists of current and voltage transformers, featuring a resistive divid
Externí odkaz:
https://doaj.org/article/af8d89b1c4b8455aafd4ccf8431f240a
Publikováno v:
MATEC Web of Conferences, Vol 178, p 09006 (2018)
This study examines the results of thermal and aerodynamic tests of a digital combined current and voltage transformer conducted in an environmental chamber. This measuring instruments consist of current and voltage transformers, featuring a resistiv
Externí odkaz:
https://doaj.org/article/8f1a49bd20d945cb940f8bfb1857000f
Autor:
Langer Christoph, Glorius Jan, Slavkovská Zuzana, Litvinov Sergey, Litvinov Yuri A., Reifarth René
Publikováno v:
EPJ Web of Conferences, Vol 165, p 01033 (2017)
Ion optical calculations for a storage ring at the present GSI facility for direct proton-induced reactions relevant for different astrophysical processes are presented. As an example case, the 59Cu(p,γ) and 59Cu(p,α) reactions are shown. The branc
Externí odkaz:
https://doaj.org/article/9284b54ae95b41f599ae1f0e6372fe4b
Publikováno v:
PNAS Nexus, Volume 3, Issue 1, January 2024, pgae005
We introduce the Optimizing a Discrete Loss (ODIL) framework for the numerical solution of Partial Differential Equations (PDE) using machine learning tools. The framework formulates numerical methods as a minimization of discrete residuals that are
Externí odkaz:
http://arxiv.org/abs/2205.04611