Journal Article

GSI-2024-00447

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Machine learning for the prediction of converged energies from ab initio nuclear structure calculations

Knöll, M. (Corresponding author) ; Wolfgruber, T. ; Agel, M. L. ; Wenz, C. ; Roth, R. (Corresponding author)GSI*

2023
North-Holland Publ. Amsterdam

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Please use a persistent id in citations: doi:10.1016/j.physletb.2023.137781  doi:10.15120/GSI-2024-00447

Note: This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/). Funded by SCOAP3

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Contributing Institute(s):

1. Helmholtz ForschAkad Hess. f. FAIR, HFHF (FHF)

Research Program(s):

1. 899 - ohne Topic (POF4-899) (POF4-899)
2. DFG project G:(GEPRIS)491382106 - Open-Access-Publikationskosten / 2022-2024 / GSI Helmholtzzentrum für Schwerionenforschung (491382106) (491382106)
3. Nuclear structure physics and nuclear astrophysics - Theory (HFHF project) (I:(DE-Ds200)HFHF-NuAstro-Th) (I:(DE-Ds200)HFHF-NuAstro-Th)

Experiment(s):

1. Experiment at GSI without experiment proposal number ( - ; Facility_outside_beam_time_program )

Appears in the scientific report 2024

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Database coverage:
; ; ; ; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; DOAJ Seal ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOAP3 sponsored Journal ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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