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EVStabilityNet: predicting the stability of star clusters in general relativity

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00008150
URN to cite this document: urn:nbn:de:bvb:703-epub-8150-6

Title data

Straub, Christopher ; Wolfschmidt, Sebastian:
EVStabilityNet: predicting the stability of star clusters in general relativity.
In: Classical and Quantum Gravity. Vol. 41 (2024) Issue 6 . - 065002.
ISSN 1361-6382
DOI der Verlagsversion: https://doi.org/10.1088/1361-6382/ad228a

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Abstract

We present a deep neural network which predicts the stability of isotropic steady states of the asymptotically flat, spherically symmetric Einstein–Vlasov system in Schwarzschild coordinates. The network takes as input the energy profile and the redshift of the steady state. Its architecture consists of a U-Net with a dense bridge. The network was trained on more than ten thousand steady states using an active learning scheme and has high accuracy on test data. As first applications, we analyze the validity of physical hypotheses regarding the stability of the steady states.

Further data

Item Type: Article in a journal
Keywords: Einstein–Vlasov; general relativity; stability; deep learning;
neural network; numerics
DDC Subjects: 500 Science > 510 Mathematics
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Professorship Applied Mathematics
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Professorship Applied Mathematics > Professor Applied Mathematics - Univ.-Prof. Dr. Gerhard Rein
Faculties
Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics
Language: English
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-epub-8150-6
Date Deposited: 24 Jan 2025 07:51
Last Modified: 24 Jan 2025 08:18
URI: https://epub.uni-bayreuth.de/id/eprint/8150

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