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Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers.

A. Khan, E. Huerta, and A. Das. CoRR, (2020)

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A E Gruber

Antonio Huerta

Rolf A E Müller

Luca E A Roncoroni

Other publications of authors with the same name

Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers.A. Khan, E. Huerta, and A. Das. CoRR, (2020)Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO.D. George, H. Shen, and E. Huerta. CoRR, (2017)Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders.H. Shen, D. George, E. Huerta, and Z. Zhao. CoRR, (2017)Observation of Gravitational Waves from a Binary Black Hole MergerB. Abbott, R. Abbott, T. Abbott, M. Abernathy, F. Acernese, K. Ackley, C. Adams, T. Adams, P. Addesso, R. Adhikari and 1001 other author(s). Phys. Rev. Lett., (February 2016)Inference-optimized AI and high performance computing for gravitational wave detection at scale.P. Chaturvedi, A. Khan, M. Tian, E. Huerta, and H. Zheng. CoRR, (2022)Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx.T. Hoang, J. Fuhrman, R. Madduri, M. Li, P. Chaturvedi, Z. Li, K. Kim, M. Ryu, R. Chard, E. Huerta and 1 other author(s). CoRR, (2023)Python Open Source Waveform Extractor (POWER): An open source, Python package to monitor and post-process numerical relativity simulations.D. Johnson, E. Huerta, and R. Haas. CoRR, (2017)Statistically-informed deep learning for gravitational wave parameter estimation.H. Shen, E. Huerta, E. O'Shea, P. Kumar, and Z. Zhao. Mach. Learn. Sci. Technol., 3 (1): 15007 (2022)A FAIR and AI-ready Higgs Boson Decay Dataset.Y. Chen, E. Huerta, J. Duarte, P. Harris, D. Katz, M. Neubauer, D. Diaz, F. Mokhtar, R. Kansal, S. Park and 3 other author(s). CoRR, (2021)Physics-inspired spatiotemporal-graph AI ensemble for gravitational wave detection.M. Tian, E. Huerta, and H. Zheng. CoRR, (2023)

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Disambiguation of "Huerta, E. A."

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Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers.

Please choose a person to relate this publication to

A E Gruber

Antonio Huerta

Rolf A E Müller

Rolf A E Müller

Luca E A Roncoroni

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Huerta, E. A."

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers.

Please choose a person to relate this publication to

A E Gruber

Antonio Huerta

Rolf A E Müller

Rolf A E Müller

Luca E A Roncoroni

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers.