Zobrazeno 1 - 2
of 2
pro vyhledávání: '"Daniel Hundhausen"'
Autor:
Erik Buhmann, Sascha Diefenbacher, Daniel Hundhausen, Gregor Kasieczka, William Korcari, Engin Eren, Frank Gaede, Katja Krüger, Peter McKeown, Lennart Rustige
Publikováno v:
Machine learning: science and technology 3(2), 025014 (2022). doi:10.1088/2632-2153/ac7848
Machine learning: science and technology 3(2), 025014 (2022). doi:10.1088/2632-2153/ac7848
Motivated by the computational limitations of simulating interactions of particles in highly-granular detectors, there exists a concerted effort to build
Motivated by the computational limitations of simulating interactions of particles in highly-granular detectors, there exists a concerted effort to build
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f33195ba3f8b709d205030b6c456ad70
https://bib-pubdb1.desy.de/record/483548
https://bib-pubdb1.desy.de/record/483548
Autor:
Anatolii Korol, Engin Eren, William Korcari, Gregor Kasieczka, Sascha Diefenbacher, Peter McKeown, Erik Buhmann, Lennart Rustige, Frank Gaede, Daniel Hundhausen, Katja Krüger
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://explore.openaire.eu/search/publication?articleId=doi_dedup___::496c6e403408c91a2e7ac690c9af5a50
https://doi.org/10.1051/epjconf/202125103049
https://doi.org/10.1051/epjconf/202125103049
