Khan, A., Huerta, E. A., & Das, A. (2020, August 25). Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers. arXiv.org. https://doi.org/10.48550/arxiv.2004.09524
Citácia podle Chicago (17th ed.)Khan, Asad, E A. Huerta, a Arnav Das. "Physics-inspired Deep Learning to Characterize the Signal Manifold of Quasi-circular, Spinning, Non-precessing Binary Black Hole Mergers." ArXiv.org 25 Aug. 2020. https://doi.org/10.48550/arxiv.2004.09524.
Citácia podľa MLA (8th ed.)Khan, Asad, et al. "Physics-inspired Deep Learning to Characterize the Signal Manifold of Quasi-circular, Spinning, Non-precessing Binary Black Hole Mergers." ArXiv.org, 25 Aug. 2020, https://doi.org/10.48550/arxiv.2004.09524.
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