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pro vyhledávání: '"Hundhausen Daniel"'
Autor:
Buhmann Erik, Diefenbacher Sascha, Eren Engin, Gaede Frank, Hundhausen Daniel, Kasieczka Gregor, Korcari William, Korol Anatolii, Krüger Katja, McKeown Peter, Rustige Lennart
Publikováno v:
EPJ Web of Conferences, Vol 251, p 03049 (2021)
Generative machine learning models offer a promising way to efficiently amplify classical Monte Carlo generators’ statistics for event simulation and generation in particle physics. Given the already high computational cost of simulation and the ex
Externí odkaz:
https://doaj.org/article/38f31f0fb1b24850a830ab78546748b2
Autor:
Bieringer, Sebastian, Butter, Anja, Diefenbacher, Sascha, Eren, Engin, Gaede, Frank, Hundhausen, Daniel, Kasieczka, Gregor, Nachman, Benjamin, Plehn, Tilman, Trabs, Mathias
Publikováno v:
JINST 17 P09028 (2022)
Motivated by the high computational costs of classical simulations, machine-learned generative models can be extremely useful in particle physics and elsewhere. They become especially attractive when surrogate models can efficiently learn the underly
Externí odkaz:
http://arxiv.org/abs/2202.07352
Autor:
Buhmann, Erik, Diefenbacher, Sascha, Eren, Engin, Gaede, Frank, Hundhausen, Daniel, Kasieczka, Gregor, Korcari, William, Krüger, Katja, McKeown, Peter, Rustige, Lennart
Motivated by the computational limitations of simulating interactions of particles in highly-granular detectors, there exists a concerted effort to build fast and exact machine-learning-based shower simulators. This work reports progress on two impor
Externí odkaz:
http://arxiv.org/abs/2112.09709
Akademický článek
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Autor:
Bieringer, Sebastian Guido, Anja Butter, Diefenbacher, Sascha Daniel, Eren, Engin, Gaede, Frank, Hundhausen, Daniel Christian, Kasieczka, Gregor, Nachman, Benjamin, Plehn, Tilman, Mathias Trabs, KIT
Motivated by the high computational costs of classical simulations, machine-learned gen- erative models can be extremely useful in particle physics and elsewhere. They become especially attractive when surrogate models can efficiently learn the under
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ecaa989b64c42b58120658101e4baebe
Autor:
Gaede, Frank, Kr��ger, Katja, Eren, Engin, Rustige, Lennart, McKeown, Peter, Buhmann, Erik, Diefenbacher, Sascha Daniel, Hundhausen, Daniel Christian, Kasieczka, Gregor, Korcari, William
Motivated by the computational limitations of simulating interactions of particles in highly-granular detectors, there exists a concerted effort to build fast and exact machine-learning-based shower simulators. This work reports progress on two impor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b099fe73d54853b22e0f08e9fe270e23