Journal Article

GSI-2024-01291

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Physics-informed neural networks for predicting the asymptotic outcome of fast neutrino flavor conversions

Abbar, S. (Corresponding author) ; Wu, M.-R. ; Xiong, Z.GSI*

2024
American Physical Society Ridge, NY

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Please use a persistent id in citations: doi:10.1103/PhysRevD.109.043024  doi:10.15120/GSI-2024-01291

Note: "Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Open access publication funded by the Max Planck Society."

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Contributing Institute(s):

1. Nukleare Astrophysik & Struktur (KNA)

Research Program(s):

1. 899 - ohne Topic (POF4-899) (POF4-899)
2. KILONOVA - Probing r-process nucleosynthesis through its electromagnetic signatures (885281) (885281)
3. DFG project G:(GEPRIS)390783311 - EXC 2094: ORIGINS: Vom Ursprung des Universums bis zu den ersten Bausteinen des Lebens (390783311) (390783311)
4. DFG project G:(GEPRIS)283604770 - SFB 1258: Neutrinos und Dunkle Materie in der Astro- und Teilchenphysik (NDM) (283604770) (283604770)

Experiment(s):

1. no experiment theory work (theory)

Appears in the scientific report 2024

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Database coverage:
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