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Gravitational-wave parameter inference using Deep Learning.

, , , , , , , и . CBMI, стр. 1-6. IEEE, (2021)

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Jose Aantonio Font-Roda

Humberto Jorge José

Nadine José Gonzalez

Jose A C Broekaert

José Navarrete

 

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AMR simulations of the low T/W bar-mode instability of neutron stars., , и . Comput. Phys. Commun., 177 (3): 288-297 (2007)Machine-Learning Love: classifying the equation of state of neutron stars with Transformers., , , , , , и . CoRR, (2022)Identification of Binary Neutron Star Mergers in Gravitational-Wave Data Using YOLO One-Shot Object Detection., , , , , , и . CoRR, (2022)Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams., , , , , и . CoRR, (2023)Gravitational-wave parameter inference using Deep Learning., , , , , , , и . CBMI, стр. 1-6. IEEE, (2021)Solving the Teukolsky equation with physics-informed neural networks, , , , и . (2024)Comparison of neural network architectures for feature extraction from binary black hole merger waveforms., , , , , и . Mach. Learn. Sci. Technol., 5 (1): 15036 (марта 2024)Numerical hydrodynamics in general relativity. (2000)cite arxiv:gr-qc/0003101 Comment: 105 pages, 12 figures. The full online-readable version of this article, including several animations, will be published in Living Reviews in Relativity at http://www.livingreviews.org.